A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning

TL;DR

This paper introduces a taxonomy and a multi-track resolution strategy for managing client-level disagreements in federated learning, ensuring client exclusion and fairness in complex multi-stakeholder environments.

Contribution

It proposes a novel, scalable approach using isolated model update tracks to handle client disagreements, validated through extensive simulation and scalability analysis.

Findings

The approach correctly handles various disagreement patterns in 34 scenarios.

Server-side resolution overhead is negligible (<1 ms per round).

Client-side training load can be mitigated with submodel reuse.

Abstract

Federated Learning (FL) typically assumes unconditional collaboration, a premise that overlooks the complexities of real-world, multi-stakeholder environments in which clients may need to exclude one another for strategic, regulatory, or competitive reasons. This paper addresses this gap, which we term 'client-level disagreements,' by first introducing a taxonomy of such scenarios. We then propose a robust, multi-track resolution strategy that guarantees strict client exclusion by creating and managing isolated model update paths ('tracks'), thereby preventing the cross-contamination and unfairness issues present in naive strategies. Through an empirical evaluation of our custom simulation system across 34 scenarios using the MNIST and N-CMAPSS datasets, we validate that our approach correctly handles permanent, temporal, and overlapping disagreement patterns. Our scalability analysis reveals the server-side resolution algorithm's overhead is negligible (<1 ms per round) even under heavy load. The primary scalability constraint is the client-side training load from participating in multiple tracks, a cost that we show can be effectively mitigated by a submodel reuse strategy. This work presents a scalable and architecturally sound method for managing client-level disagreements, and enhances the practical applicability of FL in settings where policy compliance and strategic control are non-negotiable.