arXiv Machine Learning

Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design

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.

arXiv Machine Learning

Oct 15

Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners

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.

By Drashthi Doshi, Avishek Ghosh, Swaprava Nath

arXiv Machine Learning

Jul 27

Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning

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.

By Thanh Linh Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham

safety

arXiv Machine Learning

Aug 4

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

arXiv:2608. 02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models.

By Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei Chen

safety

arXiv Machine Learning

Oct 3

Latent Information Sharing for Accelerating Federated Learning

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.

By Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos, Baturalp Buyukates, Sunwoo Lee

safety

arXiv Machine Learning

Sep 22

How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

arXiv:2412.07813v4 Announce Type: replace-cross Abstract: To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a pro...

By Joohyung Lee, Jungchan Cho, Wonjun Lee, Mohamed Seif, H. Vincent Poor

safety

arXiv Machine Learning

Jul 21

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning

arXiv:2502. 08829v2 Announce Type: replace Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines.

By Ahmed Elhussein, Florent Pollet, Gamze G\"ursoy

efficiency

arXiv Machine Learning

Jun 24

FAIRVAR: Fair Federated Learning via Variance Regularization

arXiv:2508. 12042v3 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data.

By Zahra Kharaghani, Ali Dadras, Tommy L\"ofstedt

benchmarks safety

arXiv AI

Jul 28

Multi-Agent Privacy Game in Federated Learning: A Unified Mean-Field View

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.

By Kun Zhao, Xu Chen

agents safety

arXiv Machine Learning

Sep 21

FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

arXiv:2609.21057v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic c...

By Herlock Rahimi, Dionysis Kalogerias

benchmarks safety

arXiv Machine Learning

Jun 18

SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System

arXiv:2606. 18384v1 Announce Type: new Abstract: Hierarchical Federated Learning (HFL) enables scalable collaborative model training across distributed devices while preserving data privacy.

By Seyed Salar Ghazi, Kaiwen Zhang, Mehdi feizi, Hans-Arno Jacobsen

benchmarks safety

arXiv Machine Learning

Jun 18

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk

arXiv:2502. 17748v4 Announce Type: replace Abstract: Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks.

By Tianyu Zhao, Mahmoud Srewa, Salma Elmalaki

safety

arXiv Machine Learning

Sep 30

Byzantine-Robust Federated Representation Learning

arXiv:2609.36660v1 Announce Type: new Abstract: We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients witho...

By Leonardo F. Toso, James Anderson, Rafael Pinot, Nirupam Gupta

safety

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