Democratic Learning: A Distributed Machine Learning Framework with Collaborative Voting and Model Pruning for Privacy and Security

With the rapid evolution of the Internet of Things (IoT), there is a noticeable surge in both the proliferation of edge devices and the voluminous data they generate. These edge devices are progressively furnished with AI processors, harnessing the power of deep learning to augment their data processing capabilities. However, in edge environments, traditional federated learning methods typically send multiple models to a central server for aggregation, which gives rise to several tough challenges such as low data transmission efficiency, privacy concerns, and the threat of model poisoning attacks. In this paper, we introduce a distributed machine learning framework with an innovative collaborative voting mechanism to integrate the results of adaptive pruned models on various end devices for edge computing. The main goals of this framework are to mitigate the risk of data privacy and strengthen the system’s resilience against model poisoning attacks. Additionally, an adaptive model pruning mechanism is implemented to tailor diverse models according to the limited computational resources available on end devices for enhancing training efficiency. Experiments reveal that our framework can effectively mitigate the impact of poisoning attacks, but also provide superior efficiency and accuracy for edge computing compared with other prevalent federated learning methods.