Client-Edge-Cloud Hierarchical Federated Learning

Abstract

Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.

arXiv comment6 pages, 4 figures

References in corpus (5)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Learning with Non-IID Data
• Federated Learning for Mobile Keyboard Prediction
• On the Convergence of FedAvg on Non-IID Data
• Federated Optimization in Heterogeneous Networks

Cited by in corpus (13)

• FedML: A Research Library and Benchmark for Federated Machine Learning
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
• Towards Flexible Device Participation in Federated Learning
• Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
• Communication-Efficient Edge AI: Algorithms and Systems
• Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
• HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning
• FedGL: Federated Graph Learning Framework with Global Self-Supervision
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
• Trends and Advancements in Deep Neural Network Communication
• TiFL: A Tier-based Federated Learning System
• Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning