An asynchronous federated learning focusing on updated models for decentralized systems with a practical framework
Yusuke Kanamori, Yusuke Yamasaki, Shintaro Hosoai, Hiroshi Nakamura, Hideki Takase · 2023
Federated learning (FL), a machine learning technique that preserves privacy by aggregating models from each device without exchanging personal data, has gained significant interest. This paper aims to establish an efficient and practical asynchronous FL method for decentralized systems. In asynchronous decentralized FL, the order of learning and aggregation is arbitrary. First, we explore the impact of this ordering on FL. Then, we propose to utilize the version information regarding model updates. Our strategy is to aggregate only the updated models after the previous round to improve the quality of the device’s model by avoiding the reaggregation of older models. Moreover, we also design a new practical framework for asynchronous decentralized FL by extending Flower framework. Our framework realizes effective communication ability by leveraging gRPC communication and thus can be applied to practical systems without the central server. Our evaluation shows the effectiveness of our methods that aggregate only the updated models from other devices. In addition, we show the impact of ordering on learning and aggregation according to situations.