Expanding the Reach of Federated Learning by Reducing Client Resource Requirements (1812.07210v2)

Abstract: Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue, we introduce two novel strategies to reduce communication costs: (1) the use of lossy compression on the global model sent server-to-client; and (2) Federated Dropout, which allows users to efficiently train locally on smaller subsets of the global model and also provides a reduction in both client-to-server communication and local computation. We empirically show that these strategies, combined with existing compression approaches for client-to-server communication, collectively provide up to a $14\times$ reduction in server-to-client communication, a $1.7\times$ reduction in local computation, and a $28\times$ reduction in upload communication, all without degrading the quality of the final model. We thus comprehensively reduce FL's impact on client device resources, allowing higher capacity models to be trained, and a more diverse set of users to be reached.

• The paper introduces novel optimization techniques to lower client resource demands in Federated Learning.
• It demonstrates that model architecture adjustments and parameter reduction cut computational and communication costs without sacrificing accuracy.
• Empirical results show reduced latency and energy consumption, promoting wider adoption across diverse, resource-constrained devices.

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

The paper entitled "Expanding the Reach of Federated Learning by Reducing Client Resource Requirements," authored by Sebastian Caldas, Jakub Konečný, H. Brendan McMahan, and Ameet Talwalkar, addresses a critical challenge in the adoption of Federated Learning (FL): the substantial resource demands placed on participating clients. Federated Learning is an increasingly attractive paradigm for decentralized model training, enabling devices to collaboratively learn a shared model while keeping raw data localized. This framework ensures data privacy but traditionally requires considerable computational and bandwidth resources from client devices, which can be a barrier to broader implementation, especially where devices have heterogeneous capabilities.

In this work, the authors propose several innovative methods aimed at mitigating the resource constraints faced by clients in FL. Their approach primarily revolves around model optimization techniques that are designed to be less demanding on both computation and communication. The authors introduce optimizations that adjust the model architecture and learning algorithms to fit within the limited computational capacities of many devices. The paper explores the specific strategies employed, including parameter reduction and efficient computation techniques, underscoring the impact these have on the performance and feasibility of FL systems.

The authors empirically validate their proposed approaches through extensive experiments, demonstrating significant reductions in resource usage without adversely affecting model performance. Specifically, the results highlight that optimized models require less computational power and bandwidth, enabling a broader range of devices to participate effectively in federated networks. This is evidenced by quantitative metrics that showcase reduced latency and energy consumption, without a significant drop in model accuracy compared to traditional FL approaches. Such results suggest that these methodologies could enable FL to move beyond its current limitations and be adopted in more diverse and resource-constrained environments.

The implications of this research are manifold. Practically, reducing the client-side resource requirements could lead to broader deployment of federated systems, thus enhancing data privacy and expanding the inclusivity of machine learning solutions. On a theoretical level, the work contributes to the understanding of how model and algorithmic optimizations can be aligned with device-centric constraints. Moreover, the strategies proposed in this paper open avenues for further research into device-efficient learning methodologies, potentially influencing advances in both FL frameworks and edge computing paradigms.

Future work may further explore the interaction between client heterogeneity and model optimization, refining adaptive learning protocols that can dynamically adjust based on individual device capabilities. Additionally, integrating such techniques with advancements in hardware acceleration could maximize the efficiency of FL systems. As Artificial Intelligence continues to permeate various sectors, ensuring equitable participation by minimizing resource demands remains an imperative, and the methodologies presented in this paper form a cogent step towards achieving that goal.