Abstract

Federated learning provides an effective solution to the data privacy issue in distributed machine learning. However, distributed federated learning systems are inherently susceptible to data poisoning attacks and data heterogeneity. Under conditions of high data heterogeneity, the gradient conflict problem in federated learning becomes more pronounced, making traditional defense mechanisms against poisoning attacks less adaptable between scenarios with and without attacks. To address this challenge, we design a two-stage federated learning framework for defending against poisoning attacks—FedCVG. During implementation, FedCVG first removes malicious clients using a reputation-based clustering method, and then optimizes communication overhead through a virtual aggregation mechanism. Extensive experimental results show that, compared to other baseline methods, FedCVG improves average accuracy by 4.2% and reduces communication overhead by approximately 50% while defending against poisoning attacks.

Abstract: Distributed machine learning (DML) may become an important component of IoT device fleets and smart homes in the future. However, it currently presents challenges where reliable (or frequent) internet connectivity is necessary, or trust is not handled. Since DML is generally decentralized and often relies on peer-to-peer networks, we argue that BitTorrent as a time-proven protocol in this space could aid in building a solution. This paper explores the possibilities of employing BitTorrent mechanisms for gossip-based DML. It provides initial evidence supporting the viability of this approach by analysing the behaviour of model training in a simulator representing 30 individual peers with distinct data sets.