Mitigating Distributed DoS Attacks on Bandwidth Allocation for Federated Learning in Mobile Edge Networks
Yang Xu, Shanshan Zhang, Chen Lyu, Jia Liu, Yulong Shen, Norio Shiratori · IEEE Transactions on Dependable and Secure Computing · 2024
In mobile edge networks, federated learning (FL) has garnered substantial attention as a distributed machine learning framework with significant advantages for protecting user privacy. Due to the limited resources of wireless bandwidth, such FL-based applications are quite susceptible to Distributed Denial-of-Service (DDoS) attacks. Prior solutions either rely on centralized mechanisms that require complete information about all participants or are customized to specific systems. However, these solutions are either obsolete or ineffective given the new properties of FL. In this work, we first formulate a DDoS mitigation problem on bandwidth allocation for FL within mobile edge networks. Considering interactions between various network components and users, we propose anEvolutionaryGame andDouble-sidedAuction-based framework, termed EGDA, which consists of EG-based and DA-based mechanisms for user-bandwidth allocation (UBA) and server-bandwidth allocation (SBA), respectively. Specifically, to address DDoS attacks on UBA, we design an EG-based approach with minimum latency for FL under limited information. The proposed EG-based allocation algorithm is proven to be stable and achieve the evolutionary equilibrium. To mitigate DDoS attacks on SBA, we study an approach of DA with social welfare maximization while protecting the privacy of participants. Then, an iterative DA-based allocation algorithm is developed to be convergent and satisfy desirable economic properties. Extensive evaluation demonstrates that EGDA mitigates DDoS attacks effectively and efficiently.