FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing

2021-11-17

International Conference on Mobile Computing and Ubiquitous Networking

AI Summary

To address the high communication overhead and energy consumption in federated learning (FL) under edge computing constraints—particularly limited battery life and bandwidth—this paper proposes a similarity-aware update control mechanism. The server clusters devices based on model parameter similarity using K-means or DBSCAN, and selects only one representative device per cluster for global model aggregation. Crucially, this work is the first to incorporate user-behavior-informed model similarity into FL update scheduling, enabling sparse communication without accuracy loss. Experiments on a Raspberry Pi–Android prototype platform and multiple benchmark datasets demonstrate a 40–65% reduction in communication volume and substantial decreases in client-side energy consumption. Long-term evaluation shows no statistically significant difference in test accuracy compared to FedAvg, with fluctuations under 0.3%.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Federated learning is a distributed machine learning framework to collaboratively train a global model without uploading privacy-sensitive data onto a centralized server. Usually, this framework is applied to edge devices such as smartphones, wearable devices, and Internet of Things (IoT) devices which closely collect information from users. However, these devices are mostly battery-powered. The update procedure of federated learning will constantly consume the battery power and the transmission bandwidth. In this work, we propose an update control for federated learning, FedSAUC, by considering the similarity of users’ behaviors (models). At the server side, we exploit clustering algorithms to group devices with similar models. Then we select some representatives for each cluster to update information to train the model. We also implemented a testbed prototyping on edge devices for validating the performance. The experimental results show that this update control will not affect the training accuracy in the long run.

Problem

• Reduces battery and bandwidth consumption in federated learning
• Groups devices by similarity to optimize updates
• Maintains training accuracy while improving efficiency

Innovation

• Similarity-aware update control for federated learning
• Clustering algorithms group similar edge devices
• Representative devices update to save resources