Decentralized Federated Learning for Industrial IoT With Deep Echo State Networks

Abstract

Federated learning (FL) has recently been adopted to train shared models across industrial Internet of Things (IoT) devices without revealing their private raw data. Conventional FL usually relies on a central server for coordination. However, in reality, the central server is not fully trusted, which means that it may be collecting data, raising concerns about data leakage and misuse. Here, we propose a decentralized FL algorithm based on deep neural networks to address the problem of untrusted central servers. The original problem is decomposed into several subproblems with consensus constraints, which can be solved by local computation and communication. The proposed algorithm combines the decentralized average consensus and alternating direction method of multipliers. Several decentralized algorithms are employed for comparison, and the issue of heterogeneous data is discussed. Experimental evaluation shows the effectiveness of our proposed algorithm.