TrustFed: A Reliable Federated Learning Framework with Malicious-Attack
  Resistance
Hangn Su Jianhong Zhou Xianhua Niu Gang Feng
As a key technology in 6G research, federated learning (FL) enables
collaborative learning among multiple clients while ensuring individual data
privacy. However, malicious attackers among the participating clients can
intentionally tamper with the training data or the trained model, compromising
the accuracy and trustworthiness of the system. To address this issue, in this
paper, we propose a hierarchical audit-based FL (HiAudit-FL) framework, with
the aim to enhance the reliability and security of the learning process. The
hierarchical audit process includes two stages, namely model-audit and
parameter-audit. In the model-audit stage, a low-overhead audit method is
employed to identify suspicious clients. Subsequently, in the parameter-audit
stage, a resource-consuming method is used to detect all malicious clients with
higher accuracy among the suspicious ones. Specifically, we execute the model
audit method among partial clients for multiple rounds, which is modeled as a
partial observation Markov decision process (POMDP) with the aim to enhance the
robustness and accountability of the decision-making in complex and uncertain
environments. Meanwhile, we formulate the problem of identifying malicious
attackers through a multi-round audit as an active sequential hypothesis
testing problem and leverage a diffusion model-based AI-Enabled audit selection
strategy (ASS) to decide which clients should be audited in each round. To
accomplish efficient and effective audit selection, we design a DRL-ASS
algorithm by incorporating the ASS in a deep reinforcement learning (DRL)
framework. Our simulation results demonstrate that HiAudit-FL can effectively
identify and handle potential malicious users accurately, with small system
overhead.