Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design
Participation in federated learning (FL) comes at a cost. Clients trade off privacy, communication, and compute costs for potentially greater gains in model efficacy. This paper explores this tradeoff under the aegis of individual rationality (IR) versus autarky, the basic game-theoretic requirement that the federation provide utility no worse than local training. Using the above as the design target, we examine pathwise performance of FL, as a per-round bound on cumulative surplus rather than only an asymptotic equilibrium guarantee under different degrees of client data heterogeneity. Along the learning path, clients can remain below their local-training baseline for hundreds of rounds....
| Authors | Amin Meghrazi, Srinivasan Parthasarathy, Andrew Perrault |
|---|---|
| Primary category | cs.LG |
| Published | 13 September 2026 |
| Last updated at source | 27 September 2026 |
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