Towards Byzantine-resilient Learning in Decentralized Systems

With the proliferation of IoT and edge computing, decentralized learning is becoming more promising. When designing a distributed learning system, one major challenge to consider is Byzantine Fault Tolerance (BFT). Past works have researched Byzantine-resilient solutions for centralized distributed learning. However, there are currently no satisfactory solutions with strong efficiency and security in decentralized systems. In this paper, we propose a novel algorithm, Mozi, to achieve BFT in decentralized learning systems. Specifically, Mozi provides a uniform Byzantine-resilient aggregation rule for benign nodes to select the useful parameter updates and filter out the malicious ones in each training iteration. It guarantees that each benign node in a decentralized system can train a correct model under very strong Byzantine attacks with an arbitrary number of faulty nodes. We perform the theoretical analysis to prove the uniform convergence of our proposed algorithm. Experimental evaluations demonstrate the high security and efficiency of Mozi compared to all existing solutions.