Marvel: Towards Efficient Federated Learning on IoT Devices

Abstract

Federated learning has gained significant attention as a distributed machine learning paradigm, particularly due to its ability to preserve privacy by keeping data on IoT devices and only transmitting model parameters to the central server. However, existing federated learning systems face several challenges in their deployment. These challenges can be categorized into three main areas: data relation obliviousness, model characteristic obliviousness, and low communication bandwidth. To address these issues, we propose Marvel, a novel consolidated federated learning system designed specifically for efficient model training on IoT devices. Marvel tackles the challenges by employing a series of innovative techniques. Firstly, it clusters devices based on their data similarities, leveraging data fingerprints and the Hamming distance between them. This clustering process allows Marvel to group devices with similar data, enabling more efficient model training. Once the device clustering is complete, Marvel initiates the formal model training process. During this process, Marvel dynamically selects one device from each cluster to upload model parameters based on the predicted model convergence speed. This prediction is achieved using a lightweight LSTM-based prediction model, which helps determine the most suitable devices for parameter updates. Furthermore, Marvel employs a Tucker decomposition approach to decompose the parameters before transmission in each communication round. This decomposition reduces the size of the transmitted data and enables faster communication between devices and the central server. The parameters are then reconstructed on the central server, and vice versa when dispatching model parameters to devices. We evaluate Marvel using a 180-device testbed and implement a prototype using PyTorch. The results demonstrate the effectiveness of Marvel, as it achieves an average model training speedup of 59.81% and reduces communication rounds by up to 66.67% compared to existing schemes.