Distributed Learning with Personalized Experience Replay for Federated IoT

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, such as Internet of Things (IoT) devices, without directly exchanging raw data. However, the practical deployment of FL in IoT environments is hindered by several key challenges, including significant data heterogeneity across devices and the limited computational and storage resources inherent in these devices. This paper proposes a novel distributed learning framework that addresses these limitations by incorporating personalized experience replay (PER). PER allows each IoT device to store a representative subset of its training data and periodically replay past experiences to refine its local model. This mechanism not only mitigates the effects of data heterogeneity but also improves model convergence and accuracy, particularly in scenarios where individual devices have sparse or biased data. The proposed approach maintains user privacy by operating locally on device data, and the distributed nature of the framework reduces the communication overhead typically associated with FL. We demonstrate the effectiveness of our approach through a theoretical analysis and outline the key components and design considerations for a practical implementation. The core claim is that federated learning in IoT devices faces challenges with data heterogeneity and limited computational resources. The core mechanism is to implement a distributed learning framework incorporating personalized experience replay, where each IoT device stores a subset of its training data and periodically replays experiences to improve model performance, while preserving user privacy. This approach represents a significant advancement in FL for IoT, enabling more robust and accurate models in resource-constrained environments.