Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks

Core Concepts

Optimizing task delegation in multi-server federated learning networks for fairness and efficiency.

Abstract

In the rapidly advancing field of federated learning, ensuring efficient task delegation while incentivizing client participation poses challenges. The proposed FAMuS framework addresses these issues by establishing virtual queues to track FL server access, minimizing costs while ensuring stability. Extensive experiments show FAMuS outperforms baselines in accuracy, cost reduction, and fairness.

Stats

Achieves 6.91% higher test accuracy.
Shows a 27.34% lower cost compared to the best-performing baseline.
Demonstrates a 0.63% higher fairness on average.

Quotes

"In the evolving landscape of machine learning, federated learning emerges as a promising decentralized paradigm."
"Contract Theory-based methods are designed under the assumption of a single FL server, which is unrealistic in practice."
"FAMuS combines Contract Theory and Lyapunov optimization to manage task delegation efficiently."

Key Insights Distilled From

Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks

by Yulan Gao,Ch... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09153.pdf

Deeper Inquiries

How can the FAMuS framework be adapted for different network sizes

The FAMuS framework can be adapted for different network sizes by adjusting the parameters and configurations to suit the specific requirements of the network. For larger networks, scalability becomes a crucial factor. This can be addressed by optimizing task delegation algorithms, client selection processes, and contract designs to efficiently handle a higher number of FL servers and clients. Additionally, considering the increased complexity in larger networks, advanced optimization techniques may be employed to ensure effective coordination and management of tasks across multiple servers.

What are the potential drawbacks of relying on historical data for client participation types

Relying solely on historical data for client participation types may have several potential drawbacks. One major drawback is that it may not accurately reflect real-time changes in client behavior or preferences. Clients' participation costs and types can evolve over time due to various factors such as changing priorities, resource availability, or external influences. Depending solely on historical data could lead to suboptimal decision-making and ineffective incentive mechanisms if there are significant shifts in client behaviors that are not captured in the historical data.

How can virtual queues impact the scalability of multi-server FL networks

Virtual queues can impact the scalability of multi-server FL networks by influencing system performance and resource allocation efficiency. While virtual queues help manage task delegation among FL servers based on their previous access patterns, they can also introduce complexities in maintaining stability and fairness across all servers. In large-scale networks with numerous servers and clients, managing virtual queues effectively becomes challenging as it requires continuous monitoring and adjustment to prevent bottlenecks or delays in task processing. Additionally, virtual queues add an overhead in terms of computational resources needed for queue management which can affect overall scalability if not optimized properly.