MR-FFL: A Stratified Community-Based Mutual Reliability Framework for Fairness-Aware Federated Learning in Heterogeneous UAV Networks

Fairness-aware federated learning (FFL) plays a crucial role in mitigating bias against specific demographic groups (e.g., gender, race, and occupation) during collaborative training. Along with the ever-emerging new attack paradigms like gradient leakage and model poisoning, the reliability of FFL also obtains lots of research attention. Either UAV nodes or FFL aggregators could be untrusted adversaries. Although multiple security mechanisms involving encryption, obfuscation, Byzantine-robustness, and detection have been proposed, concrete to UAV networks, the majority of existing solutions are unfeasible due to high heterogeneity and limited resources among participants. Hence, in this article, we propose mutually reliable FFL (MR-FFL), a stratified community-based framework to facilitate privacy protection (FFL aggregator's reliability) and poisoning elimination (client nodes' reliability) jointly for FFL in heterogeneous UAV networks. We first divide UAV nodes into both peer communities (PCs) and colleague communities (CCs) according to cross-participant similarity and task-oriented fitness, respectively. Thus, the arbitrarily settled learning tasks following fair principles can be efficiently completed by fine-tuned CCs, even in the presence of a large degree of heterogeneity among PCs. Then, we integrate community-specific differential privacy into the MR-FFL process, to achieve privacy amplification as well as efficient and personal collaborative training at the same time. More importantly, we proposed a community-based credit evaluation to resist poisoning attacks in heterogeneous environments. The results on several standard data sets also highlight the performance of MR-Fed in terms of fairness, accuracy, and integrity jointly.