Byzantine-robust and efficient federated learning for internet-of-things

Abstract

The expansion of Internet-of-Things (IoT) devices with a wealth of generated data opens up new possibilities for intelligent IoT applications (i.e., smart home and smart transportation), but the increasing concern about data privacy makes the enabling force of intelligent IoT, machine learning (ML), harder to deploy. Federated learning (FL), an emerging distributed ML paradigm that allows on-device ML model training without sharing private raw data, is becoming a promising solution to achieve collaborative intelligence in IoT. However, the privacy-preserving design of FL makes it vulnerable to Byzantine workers who behave arbitrarily and send poisonous model updates to the central server to corrupt the joint learning process. Moreover, traditional FL suffers from massive communications overhead due to the large number of training rounds to convergence with non-independent identically distributed (non-i.i.d.) data across clients. To address these problems, we propose a novel robust aggregation rule for FL, federated mutual information (FedMI) which leverages the mutual information between clients to build resilience to Byzantine workers and accelerate the convergence speed. We perform experiments over the non-i.i.d FEMNIST dataset under different adversarial settings. Experimental results demonstrate the effectiveness of the proposed FedMI: much faster convergence speed and higher defense capability when compared to the state-of-the-art robust aggregation rules for FL.