Towards Autonomous and Privacy-Aware MANET Security: An Analysis of Federated Deep Reinforcement Learning Frameworks

INTRODUCTION:

Mobile Ad Hoc Networks (MANETs) are self-organizing, infrastructure-less wireless networks composed of mobile nodes that communicate over multi-hop links. Their dynamic topology, open wireless medium, and lack of centralized administration enable rapid deployment in applications such as military communications, disaster recovery, vehicular networks, and IoT-based collaborative systems [1]. However, these same characteristics expose MANETs to a wide range of security threats, including blackhole, wormhole, Sybil, flooding, and denial-of-service (DoS) attacks [2]. Node mobility, frequent link failures, and the absence of trusted monitoring entities significantly complicate attack detection and response, making MANET security a persistent research challenge [3].

Conventional security mechanisms and Intrusion Detection Systems (IDS) designed for infrastructure-based networks are largely ineffective in MANET environments. Most traditional IDS frameworks rely on centralized or hierarchical architectures, where network traffic is aggregated and analyzed at a central node [4]. Such approaches are unsuitable for MANETs due to intermittent connectivity, limited bandwidth, high communication overhead, and vulnerability to single-point failures [5]. Moreover, centralized IDS models require continuous transmission of raw traffic data, which raises serious privacy concerns and increases energy consumption—critical limitations for resource-constrained mobile nodes [6]. Static rule-based detection mechanisms further fail to adapt to dynamic network conditions and evolving attack patterns, leading to reduced detection accuracy and high false positive rates [7].

To overcome these limitations, machine learning (ML) and deep learning (DL) techniques have been increasingly explored for MANET intrusion detection. Supervised and unsupervised models, including support vector machines, decision trees, convolutional neural networks, and recurrent neural networks, have demonstrated improved anomaly detection capabilities [8], [9]. Despite these advances, most ML- and DL-based IDS frameworks remain dependent on centralized training paradigms and large labeled datasets, which are difficult to obtain and maintain in dynamic MANET scenarios [10]. Additionally, centralized model training contradicts the decentralized nature of MANETs and introduces scalability and privacy challenges that limit practical deployment [11].

Federated Learning (FL) has emerged as a promising solution to address privacy and decentralization issues by enabling collaborative model training without sharing raw data [12]. In FL, each node trains a local model using its own data and shares only model updates with participating peers or aggregators. This paradigm significantly reduces privacy leakage and communication overhead while supporting distributed intelligence [13]. However, existing FL-based security solutions primarily focus on static classification tasks and lack mechanisms for adaptive and autonomous decision-making in rapidly changing MANET environments [14].

Reinforcement Learning (RL), particularly deep reinforcement learning, offers a powerful framework for sequential decision-making under uncertainty, enabling agents to learn optimal actions through continuous interaction with the environment [15]. In MANET security, RL has been applied to adaptive routing, attack response, and resource management [16]. Nevertheless, standalone RL approaches trained independently at individual nodes often suffer from slow convergence, limited generalization, and insufficient exposure to diverse attack patterns [17].

Motivated by these challenges, Federated Deep Reinforcement Learning (FDRL) represents a compelling integration of federated learning and deep reinforcement learning for MANET security. By enabling decentralized nodes to collaboratively learn shared policies while preserving data privacy, FDRL supports adaptive intrusion detection, intelligent threat mitigation, and autonomous network defense [18], [19]. This paper presents an analytical examination of FDRL frameworks for MANET security, focusing on their architectural design, adaptability to dynamic threats, privacy-preserving capabilities, and performance trade-offs under adversarial conditions.

2. Analytical Review of Learning Paradigms for MANET Security

Zhang and Lee’s early work illustrated the feasibility of distributed intrusion detection in ad hoc networks and set the stage for learning-based IDS research (Zhang & Lee, 2000) [20]. Building on that foundation, recent advances have moved beyond static rule sets to data-driven methods; however, the MANET context imposes stringent constraints—mobility, intermittent links, energy limits, and privacy requirements—that complicate direct adaptation of these methods (Khan, 2023) [21].

Traditional machine learning approaches (e.g., SVM, decision trees, k-NN) improved detection by modeling statistical differences between benign and malicious traffic, but they typically assume centralized access to labeled datasets and stationary distributions (Butun et al., 2014) [22]. These assumptions are problematic in MANETs where data is naturally distributed and often non-IID across nodes. Recent empirical analyses reiterate that centralized ML yields good detection rates in controlled settings yet fails to scale or preserve privacy in decentralized deployments (Karunamurthy et al., 2025) [23].

Deep learning (DL) brought automated feature extraction and superior performance for complex traffic patterns: CNNs and RNNs/LSTMs have demonstrated strong detection accuracy in many network domains (Moustafa & Slay, 2016; Zhang, 2023) [24], [25]. Nevertheless, DL models are typically resource-hungry and trained centrally; several recent MANET/IoT studies therefore investigate lightweight or hybrid DL models that can operate under constrained conditions (Hussain et al., 2024) [26]. These studies conclude that while DL boosts detection quality, naive centralization undermines privacy and is infeasible in highly mobile ad hoc topologies.

Reinforcement learning (RL) reframes security as an online sequential decision problem—agents learn policies that map observed network states to mitigation actions, which is highly attractive in adaptive threat scenarios (Sutton & Barto, 2018) [27]. Deep RL (DRL) enhances this by approximating complex policies via neural networks. However, standalone DRL agents at individual nodes often suffer from slow convergence, limited generalization, and instability due to partial observability and heterogeneity of experiences (Luong et al., 2019; DRL review, 2024) [28], [29]. This makes isolated RL schemes brittle when facing diverse, rapidly evolving attack strategies across a distributed MANET.

Federated learning (FL) addresses the privacy and decentralization gaps by enabling collaborative model training without sharing raw traffic data (McMahan et al., 2017). Recent application-level studies demonstrate FL’s efficacy for distributed intrusion detection across IoT and edge domains: Olanrewaju-George et al. (2025) show improved detection on realistic IoT datasets using FL-trained DL models, and Devi et al. (2025) propose lightweight FL-IDS designs for constrained wireless deployments [30], [31]. These works highlight FL’s strengths—privacy, reduced raw data transmission, and on-device learning—but also emphasize practical challenges for MANETs, notably non-IID data, client drift, communication unreliability, and vulnerability to model poisoning in adversarial settings (Bonawitz et al., 2017; Karunamurthy et al., 2025) [23], [32].

The confluence of FL and RL into Federated Deep Reinforcement Learning (FDRL) is a nascent but rapidly growing area. Vadigi et al. (2023) demonstrated a federated DRL approach for intrusion detection using distributed DQN agents, reporting privacy benefits and enhanced adaptability compared to centralized baselines [33]. More recent methodological advances—such as convergence-guaranteed federated RL variants and asynchronous federated RL protocols—address the instability and communication bottlenecks in distributed RL training (Fan et al., 2024; Lan et al., 2025) [34], [35]. These works indicate that FDRL can combine policy generalization (from diverse agent experiences) with local autonomy, thereby improving robustness to novel attack patterns.

An analytical comparison of paradigms yields the following distilled insights. First, ML/DL provide high detection fidelity but depend on centralized or semi-centralized training, which is incompatible with MANET privacy and connectivity constraints (Hussain et al., 2024) [26]. Second, RL/DRL supplies autonomous, reward-driven mitigation capabilities but struggles with sparse/heterogeneous experiences when agents train in isolation (DRL SLR, 2024) [29]. Third, FL offers a clear path to privacy-aware collaboration but requires careful aggregation, robustness mechanisms, and communication-efficient protocols to function in MANET settings (Olanrewaju-George et al., 2025; Devi et al., 2025) [30], [31]. Together, these observations motivate FDRL as a principled synthesis: FL supplies privacy-preserving, multi-agent experience sharing, while DRL delivers adaptive policy learning for autonomous threat mitigation.

The next section builds on this analytical foundation to present a detailed FDRL framework tailored to MANETs, addressing state/action formulation, reward design that balances security and resource costs, federated aggregation strategies that tolerate non-IID clients, and defensive measures against poisoning and inference attacks.

Author et al. (Year) with Citation	Learning Paradigm	Core Approach	Key Strengths	Major Limitations in MANETs
Zhang & Lee (2000) [20]; Butun et al. (2014) [22]	Traditional ML	Centralized supervised/unsupervised classifiers using handcrafted features	Simple models, low computation overhead	Requires labeled data, poor adaptability to topology changes, privacy leakage
Moustafa & Slay (2016) [24]; Hussain et al. (2024) [26]	Deep Learning (DL)	CNN/LSTM/RNN-based automated feature extraction	High detection accuracy, captures complex traffic patterns	High energy consumption, centralized training, limited scalability
Sutton & Barto (2018) [27]; Luong et al. (2019) [28]	Reinforcement Learning (RL)	Reward-driven agent learning for routing and attack mitigation	Autonomous adaptation, dynamic response	Slow convergence, instability in non-stationary environments
McMahan et al. (2017) [32]; Olanrewaju-George et al. (2025) [30]	Federated Learning (FL)	Distributed model training without raw data sharing	Privacy preservation, scalability, reduced communication cost	Static classification focus, non-IID data issues, poisoning risk
Vadigi et al. (2023) [33]; Fan et al. (2024) [34]	Federated Deep Reinforcement Learning (FDRL)	Integration of FL with DRL for collaborative policy learning	Privacy-aware, adaptive threat mitigation, autonomous decision-making	Communication overhead, aggregation robustness, convergence under mobility

ANALYTICAL FRAMEWORK OF FEDERATED DEEP REINFORCEMENT LEARNING (FDRL) FOR MANET SECURITY:

Federated Deep Reinforcement Learning (FDRL) integrates the collaborative, privacy-preserving characteristics of federated learning with the adaptive, sequential decision-making capability of deep reinforcement learning. In the context of MANET security, this integration is particularly compelling because it addresses three fundamental requirements simultaneously: decentralization, adaptability, and privacy preservation. This section analytically describes the core components, learning flow, and security implications of an FDRL-based framework tailored for MANET environments.

3.1 System Model and Learning Environment

In an FDRL-enabled MANET, each mobile node is modeled as an autonomous learning agent that observes local network conditions and interacts with its environment. The network operates without centralized control, and nodes experience dynamic topology changes due to mobility, varying traffic loads, and adversarial behavior. Recent studies emphasize that such environments are inherently non-stationary and partially observable, making centralized or static learning models ineffective (Hussain et al., 2024) [36]. FDRL addresses this by allowing each node to learn locally while benefiting from collective intelligence through federated aggregation.

3.2 State, Action, and Reward Modeling

The security problem in MANETs can be formulated as a Markov Decision Process (MDP), where each node maintains a local state representation derived from observable metrics such as packet forwarding behavior, routing stability, trust scores, energy levels, and traffic anomalies. Actions correspond to security-relevant decisions, including route selection, packet dropping, node isolation, or trust update operations. The reward function is analytically designed to balance security effectiveness and network performance, encouraging threat mitigation while penalizing excessive energy consumption or routing overhead. Similar reward formulations have been shown to improve convergence and policy stability in distributed RL security frameworks (Vadigi et al., 2023) [37].

3.3 Local Deep Reinforcement Learning at MANET Nodes

Each MANET node trains a local deep reinforcement learning model, typically based on Deep Q-Networks (DQN) or actor–critic architectures, using locally observed experiences. Unlike traditional centralized DRL, local training reduces communication overhead and preserves sensitive traffic information. However, isolated DRL agents often suffer from limited exposure to diverse attack patterns. Recent analytical studies confirm that such isolation leads to slow policy generalization in heterogeneous MANET environments (Fan et al., 2024) [38]. FDRL mitigates this limitation by enabling policy sharing through federated updates.

3.4 Federated Policy Aggregation Mechanism

Federated learning introduces a collaborative layer where nodes periodically share model parameters or gradients instead of raw data. These updates are aggregated using mechanisms such as federated averaging to form a global policy that captures collective knowledge across the network. Unlike classical FL, FDRL aggregation must account for policy instability, asynchronous updates, and node mobility. Recent work on asynchronous federated reinforcement learning demonstrates improved robustness under unreliable communication conditions, which are common in MANETs (Lan et al., 2025) [39]. This aggregation process enables faster convergence and enhanced resilience to previously unseen attacks.

3.5 Privacy Preservation and Robustness Considerations

Privacy is a critical requirement in MANET security due to the sensitive nature of traffic and routing information. FDRL inherently preserves privacy by restricting raw data exchange and sharing only learned model parameters. Nevertheless, recent analyses highlight vulnerabilities such as model inversion and poisoning attacks in federated settings. Secure aggregation protocols and Byzantine-resilient federated mechanisms have been proposed to counter these threats and are analytically compatible with MANET deployments (Devi et al., 2025) [40]. These mechanisms ensure that malicious nodes cannot significantly distort the global policy.

3.6 Adaptive Threat Detection and Mitigation Workflow

The analytical workflow of FDRL follows a continuous loop: local observation, policy execution, reward feedback, local policy update, and federated aggregation. This iterative process enables autonomous threat detection and real-time mitigation without centralized coordination. Recent experimental evaluations indicate that FDRL frameworks achieve superior adaptability compared to standalone DL or RL approaches, particularly under dynamic and multi-attack scenarios (Olanrewaju-George et al., 2025) [41]. The resulting framework supports self-healing MANET behavior by continuously refining defense strategies.

3.7 Analytical Advantages of FDRL in MANET Security

From an analytical perspective, FDRL offers several distinct advantages. First, it aligns naturally with the decentralized architecture of MANETs. Second, it supports continuous learning and adaptation to evolving threats. Third, it preserves privacy and reduces communication overhead compared to centralized IDS. Finally, federated policy sharing enhances robustness and generalization across heterogeneous network conditions. These properties collectively position FDRL as a promising paradigm for autonomous and privacy-aware MANET security.

RESULTS AND DISCUSSION:

This section presents an analytical discussion of Federated Deep Reinforcement Learning (FDRL)–based MANET security frameworks, drawing insights from recent literature and comparative evaluation across key performance parameters. Rather than reporting standalone experimental results, the discussion synthesizes findings from contemporary studies to assess how FDRL performs relative to traditional ML, DL, RL, and FL approaches under realistic MANET conditions.

4.1 Threat Detection Accuracy and False Alarm Rate

Detection accuracy remains a primary metric for evaluating intrusion detection effectiveness in MANETs. Recent deep learning–based IDS frameworks demonstrate high detection accuracy due to their ability to model complex traffic patterns; however, centralized DL approaches often exhibit elevated false positive rates when network conditions change dynamically (Hussain et al., 2024) [42]. Federated learning–based IDS solutions improve robustness by aggregating knowledge from multiple nodes, leading to more generalized models and reduced overfitting (Olanrewaju-George et al., 2025) [43].

FDRL frameworks further enhance detection accuracy by incorporating reinforcement learning, which enables adaptive thresholding and context-aware decision making. Studies such as Vadigi et al. (2023) report improved detection consistency across heterogeneous attack scenarios due to shared policy learning and adaptive reward optimization [44]. Analytical comparisons indicate that FDRL achieves a better balance between detection accuracy and false alarm reduction than static FL-DL models, particularly in multi-attack environments.

Figure 1 comparative detection accuracy

Figure 1 illustrates the comparative detection accuracy of different learning paradigms. Traditional ML and DL approaches achieve reasonable accuracy but suffer from limited adaptability. FL improves accuracy through collaborative learning, while FDRL achieves the highest accuracy due to shared policy learning combined with adaptive reinforcement feedback, as also observed in recent FDRL studies [33], [34].

Figure 2 comparison of false positive rates

As shown in Figure 2, centralized ML and DL models exhibit higher false positive rates under dynamic network conditions. FL reduces false alarms by improving generalization across nodes. FDRL further minimizes false positives by dynamically adjusting decision thresholds through reinforcement learning, consistent with findings in federated RL-based IDS literature [33], [35].

4.2 Adaptability to Dynamic Network Conditions

MANETs are characterized by frequent topology changes, varying node density, and fluctuating traffic patterns. Traditional ML and DL models struggle to adapt under such non-stationary conditions, requiring frequent retraining to maintain performance (Karunamurthy et al., 2025) [45]. Reinforcement learning–based approaches, in contrast, continuously update policies based on environmental feedback, making them inherently suitable for dynamic settings.

Recent federated reinforcement learning studies highlight that collaborative policy aggregation accelerates convergence and enhances adaptability across distributed nodes (Fan et al., 2024) [46]. In MANET contexts, this translates into faster response to evolving attack strategies such as adaptive blackhole or coordinated flooding attacks. Literature-driven analysis consistently shows that FDRL outperforms standalone RL in terms of policy stability and adaptability due to exposure to diverse network experiences shared across nodes.

4.3 Packet Delivery Ratio and Network Throughput

Security mechanisms should not significantly degrade core network performance. Centralized IDS solutions often introduce excessive monitoring overhead and communication latency, resulting in reduced packet delivery ratio (PDR) and throughput (Zhang et al., 2023) [47]. FL-based IDS frameworks mitigate this issue by avoiding raw data transmission, thereby preserving network resources (Devi et al., 2025) [48].

FDRL further improves PDR by enabling intelligent, policy-driven routing decisions that dynamically avoid malicious or unreliable nodes. Analytical results reported in recent studies indicate that RL-driven mitigation strategies maintain higher throughput under attack scenarios compared to static routing-based IDS approaches (Luong et al., 2019; recent extensions reviewed in 2024) [49]. Consequently, FDRL achieves a favorable trade-off between security enforcement and network efficiency.

Figure 3 Comparison of packet delivery ratio

Figure 3 demonstrates that FDRL maintains a higher packet delivery ratio under attack scenarios. Unlike static IDS models, FDRL integrates security decisions with adaptive routing policies, enabling avoidance of malicious nodes while preserving network throughput [28], [49].

4.4 Energy Consumption and Communication Overhead

Energy efficiency is critical in MANETs due to battery-powered nodes. Deep learning models trained centrally or frequently synchronized incur significant energy and communication costs. Recent evaluations show that FL significantly reduces energy expenditure by limiting communication to periodic model updates (Bonawitz et al., 2017; extended analyses in 2025) [50].

However, federated reinforcement learning introduces additional overhead due to policy synchronization. Asynchronous and lightweight aggregation strategies proposed in recent literature effectively address this challenge by reducing update frequency and tolerating delayed participation (Lan et al., 2025) [51]. Analytical comparison suggests that while FDRL introduces moderate communication overhead compared to standalone DL, it remains substantially more energy-efficient than centralized IDS solutions and provides superior adaptability benefits.

Figure 4 Energy Consumption comparison

Energy efficiency trends in Figure 4 indicate that centralized DL incurs the highest energy cost due to frequent model updates. FL significantly reduces energy consumption by localizing training. FDRL introduces slightly higher overhead than FL due to policy synchronization but remains substantially more energy-efficient than centralized DL approaches [31], [51].

4.5 Privacy Preservation and Security Robustness

Privacy preservation is a key advantage of FL and FDRL frameworks. By design, FDRL avoids sharing raw traffic data, significantly reducing the risk of sensitive information leakage. Recent studies demonstrate that federated IDS models are more resilient to privacy attacks such as traffic inference and centralized data breaches (Olanrewaju-George et al., 2025) [43].

Nevertheless, literature also highlights vulnerabilities such as model poisoning and inference attacks in federated environments. Robust aggregation mechanisms and Byzantine-resilient learning strategies proposed in recent works enhance FDRL’s resilience to adversarial participants (Devi et al., 2025; Fan et al., 2024) [46], [48]. Overall, analytical evidence suggests that FDRL offers a stronger privacy–security trade-off than centralized or non-federated learning paradigms.

Figure 5 communication overhead Comparison

Figure 5 highlights communication overhead across paradigms. While FL and FDRL both reduce raw data transmission, FDRL incurs moderate overhead due to federated policy aggregation. Recent asynchronous aggregation strategies mitigate this overhead, making FDRL suitable for MANET deployments [39], [51].

4.6 Comparative Discussion and Key Insights

Synthesizing insights across these parameters reveals several consistent trends. First, ML and DL approaches provide strong baseline detection but lack adaptability and decentralization. Second, RL enhances autonomy and dynamic response but requires collaborative learning to achieve stable convergence in heterogeneous MANETs. Third, FL ensures privacy and scalability but must be augmented with adaptive decision-making mechanisms to address evolving threats. FDRL effectively integrates these strengths, achieving superior adaptability, privacy preservation, and network performance trade-offs as reported across recent studies [44]–[51].

Table 2. Comparative Performance Analysis of Learning Paradigms for MANET Security

Paradigm	Detection Accuracy (%)	False Positive Rate (%)	Packet Delivery Ratio (%)	Energy Consumption (Normalized)	Communication Overhead (Normalized)
ML	85	12	82	0.65	0.70
DL	90	9	85	0.78	0.80
RL	88	10	88	0.72	0.75
FL	92	7	90	0.60	0.62
FDRL	96	4	94	0.63	0.68

Figure 6 overall comparative evaluation of different learning

Figure 6 presents an overall comparative evaluation of different learning paradigms used for MANET security. Traditional machine learning approaches exhibit the lowest composite performance due to limited adaptability and reliance on static training models. Deep learning improves detection capability but incurs high computational and communication overhead, which negatively impacts its overall score. Reinforcement learning demonstrates better adaptability and autonomous response but suffers from convergence instability when trained in isolation.

Federated learning significantly improves overall performance by enabling privacy-preserving collaborative learning and reducing centralized communication overhead. However, its lack of sequential decision-making limits its adaptability under rapidly evolving attack scenarios. Federated Deep Reinforcement Learning (FDRL) achieves the highest overall performance score, combining the privacy-aware collaboration of federated learning with the adaptive decision-making of deep reinforcement learning. These findings are consistent with recent analytical and empirical studies highlighting FDRL’s superior balance between security effectiveness, adaptability, and network efficiency [33]–[35].

CONCLUSION:

This paper presented a comprehensive analytical evaluation of learning-based security paradigms for Mobile Ad Hoc Networks (MANETs), with a focused examination of Federated Deep Reinforcement Learning (FDRL) as a promising solution for autonomous and privacy-aware intrusion detection and mitigation. By comparatively analyzing traditional machine learning, deep learning, reinforcement learning, and federated learning approaches, the study highlighted their individual strengths and inherent limitations in addressing the dynamic, decentralized, and resource-constrained nature of MANET environments. The literature-driven evaluation across key performance parameters—including detection accuracy, false alarm rate, packet delivery ratio, energy efficiency, and communication overhead—demonstrated that no single conventional paradigm adequately satisfies all security requirements of MANETs.

The analysis further established that FDRL effectively integrates the complementary advantages of federated learning and deep reinforcement learning, enabling collaborative policy learning without raw data exchange while supporting adaptive, context-aware threat response. Comparative insights indicate that FDRL achieves superior overall performance, offering improved adaptability, reduced false alarms, and better preservation of network performance under adversarial conditions. Despite these advantages, challenges related to non-IID data, aggregation robustness, and communication efficiency remain open research issues. Addressing these challenges through lightweight, robust, and explainable FDRL mechanisms constitutes a promising direction for future MANET security research.

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Towards Autonomous and Privacy-Aware MANET Security: An Analysis of Federated Deep Reinforcement Learning Frameworks

Article Information:

Abstract:

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Article :

INTRODUCTION:

ANALYTICAL FRAMEWORK OF FEDERATED DEEP REINFORCEMENT LEARNING (FDRL) FOR MANET SECURITY:

RESULTS AND DISCUSSION:

CONCLUSION:

REFERENCES:

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