The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper proposes mechanisms for federated learning that both incentivize strategic data owners to contribute fully and learn a high‑quality model. The first mechanism, UPBReD, jointly updates client contributions and the model, converging to a Nash equilibrium that may involve under‑contribution. The second mechanism, 2P‑UPBReD, introduces budget‑balanced transfers to make full contribution a dominant strategy, ensuring convergence to the optimal model while guaranteeing that all clients prefer to participate. Subsequent extensions (T‑2P‑UPBReD) further enforce truthful reporting of contributions and gradients without running a deficit, and experiments on CIFAR‑10, FEMNIST, and Twitter demonstrate that 2P‑UPBReD achieves welfare comparable to FedAvg trained on all data.
arXiv:2607. 23029v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent concern because the shared model updates can leak information about local datasets.
The paper investigates the trade‑off between the costs of participating in federated learning (privacy, communication, compute) and the potential gains in model performance, framing this as a game‑theoretic problem of individual rationality versus autarky. It shows that clients can remain below their local‑training baseline for many rounds and that simply capping per‑round contributions harms learning. The authors propose a new mechanism that provides short‑term participation guarantees and personalized model evaluation, demonstrating theoretically and empirically that clients can avoid short‑term losses without significantly harming overall performance, even under moderate heterogeneity.
The paper introduces a latent information sharing scheme for federated learning that mitigates client drift by sharing a small amount of hidden‑layer activations. The authors demonstrate both theoretically and empirically that this approach improves training efficiency while maintaining convergence guarantees and data privacy. Compared to existing methods such as FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, the proposed method achieves higher model accuracy within a fixed round budget without adding significant communication overhead.
arXiv:2606. 18384v1 Announce Type: new Abstract: Hierarchical Federated Learning (HFL) enables scalable collaborative model training across distributed devices while preserving data privacy.
arXiv:2503. 07869v4 Announce Type: replace Abstract: Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.