A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning
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 simulatio...
| Authors | Daan Rosendal, Ana Oprescu |
|---|---|
| Primary category | cs.DC |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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