Knowledge-Free Correlated Agreement for Incentivizing Federated Learning
We introduce Knowledge-Free Correlated Agreement (KFCA) to reward client contributions in federated learning (FL) without relying on ground truth, a public test set, or distribution knowledge. Under categorical reports and an honest majority, KFCA is strictly truthful, addressing the label-flipping vulnerability of Correlated Agreement (CA). We evaluate KFCA on federated LLM adapter tuning and a real-world PCB inspection task, showing efficient real-time reward computation suitable for decentralized and blockchain-based incentive designs.
| Authors | Leon Witt, Togrul Abbasli, Kentaroh Toyoda et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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