Client-Edge-Cloud Hierarchical Federated Learning (1905.06641v2)

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.

• The paper introduces a hierarchical federated learning system that aggregates model updates at edge servers, reducing communication load to the cloud.
• The methodology employs the HierFAVG algorithm, ensuring convergence for both convex and non-convex functions through adjustable aggregation intervals.
• Experimental results on datasets like MNIST and CIFAR-10 demonstrate reduced training time and energy consumption, highlighting its practical efficiency.

Client-Edge-Cloud Hierarchical Federated Learning: An Overview

The paper "Client-Edge-Cloud Hierarchical Federated Learning" addresses the challenges and opportunities associated with Federated Learning (FL), a collaborative machine learning framework that allows for training models across distributed devices without directly accessing the private data of clients. The authors propose a novel Client-Edge-Cloud hierarchical FL system which optimizes model training by leveraging both edge and cloud computing resources.

Key Contributions and Methodology

The primary contribution is the introduction of a hierarchical FL system that incorporates multiple edge servers between the clients and a central cloud server. This system optimizes communication efficiency and computational load by allowing model aggregation at the edge level before sending aggregated results to the cloud. The authors propose the HierFAVG algorithm to facilitate this hierarchical aggregation process and provide convergence guarantees for both convex and non-convex loss functions.

HierFAVG Algorithm

The HierFAVG algorithm is an extension of the Federated Averaging (FAVG) algorithm. In HierFAVG, local updates are periodically aggregated at edge servers, which subsequently send aggregated results to the cloud server. This multi-tier aggregation strategy aims to reduce the communication load to the cloud and improve overall training efficiency.

Convergence Analysis

The paper presents a rigorous convergence analysis for the HierFAVG algorithm. It is shown that the algorithm maintains convergence for both convex and non-convex functions under certain conditions. Key parameters, such as aggregation frequencies at different levels, are scrutinized to understand their impact on convergence and performance. The analysis implies that reducing the aggregation interval at the edge server enhances training speed without significant performance loss when edge datasets are IID.

Experimental Validation

Empirical experiments on standard datasets like MNIST and CIFAR-10 demonstrate the efficacy of the proposed hierarchical system. Results indicate that the introduction of edge servers can significantly reduce both model training time and energy consumption on client devices compared to traditional cloud-based FL systems. In scenarios with non-IID data distributions, careful tuning of aggregation intervals is shown to yield optimal performance.

Implications and Future Directions

The hierarchical FL system proposed in this paper offers a promising approach to balancing the trade-offs between communication efficiency and computational load in distributed learning environments. By reducing reliance on cloud communications, the system is well-suited for real-world applications where latency and energy constraints are pivotal.

Future research may explore adaptive strategies for aggregation frequency and resource allocation within the hierarchical framework. Additionally, extensions to more heterogeneous environments and security considerations in hierarchical FL systems may present valuable avenues for further investigation. The framework may also inspire developments in other decentralized systems, leveraging edge computing to bolster FL capabilities.

In conclusion, the integration of edge computing into the traditional FL paradigm presents a potent solution for enhancing the efficiency of distributed learning processes, meriting further exploration and development.