P2P-Fed: A Decentralized Federated Learning Platform on Structured Peer-to-Peer Systems
Ahyeon Lim, Sooho Jang, Jaehwan Lee · 2025
Federated learning is a distributed deep learning method that trains models without sending local private training data to a server, achieving communication efficiency and security. However, current federated learning techniques have two key issues: 1) scalability limitations due to heavy traffic concentrated on the central server and 2) performance degradation caused by systems and data heterogeneity. To address these issues, we propose P2P-Fed, a decentralized federated learning approach with asynchronous aggregation to minimize delays and enhance concurrency for faster convergence. We adopt Chord, a popular distributed hash table protocol, to reduce the load on individual nodes in large-scale environments and ensure stable performance in high-churn networks. We designed and implemented this system to run in a real distributed environment. Experiments in various realistic scenarios demonstrated that P2P-Fed achieved up to a 6.9% performance improvement compared to the best-performing baseline algorithm, without incurring additional overhead.