The paper studies federated learning where honest clients have heterogeneous data-generating models and adversarial clients can exacerbate this heterogeneity by sending arbitrary updates. It derives new bounds on gradient heterogeneity for linear and nonlinear regression, separating effects from honest clients’ model differences, label noise, and initialization. The authors show that for any (f,κ)-robust aggregator with κ = O(f/n) (where f is the number of adversarial clients and n the total number of clients, with f/n < 1/2), convergence is guaranteed after an explicit sample burn‑in period.
By Leonardo F. Toso, James Anderson, Nirupam Gupta, Rafael Pinot
The paper investigates how multiple optimal heads in federated averaging affect the global model in multivariate regression with private backbones and a shared linear head. By introducing a natural selection rule—each client returns the head closest to the broadcast head—and analyzing an unconstrained feature model, the authors show that the shared Gram matrix converges to the Bures‑Wasserstein barycenter of the clients’ optimal Gram matrices, which generally differs from the centralized optimum. A one‑time exchange of target means and covariances can correct this gap, restoring the centralized optimal Gram matrix in a single round under exact local optimization.
By Chuang Ma, Tomoyuki Obuchi
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
In federated averaging, local objectives can admit multiple optimal heads, making the aggregate depend on which heads clients return. We study this ambiguity in federated multivariate regression with...
The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
By Wenlong Ji, Lihua Lei, Asher Spector
arXiv:2608. 09074v1 Announce Type: cross Abstract: We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB).
By Jae Ho Chang, Arnab Auddy, Subhadeep Paul
arXiv:2607. 04085v1 Announce Type: cross Abstract: Routing-prediction federated learning has emerged as a new paradigm that reframes inter-client heterogeneity as a resource for system-level intelligence: at inference time, the server routes each external query to the best-matched client for prediction.
By Zijian Wang, Pengfei Li, Guangyu Yang, Qiong Zhang
FedFIbOS introduces a Fisher‑importance based criterion for selecting submodel parameters in heterogeneous federated learning, addressing the lack of theoretical justification in prior heuristic methods. By deriving a Fisher‑weighted quadratic masking surrogate and showing that the raw Fisher top‑k rule satisfies this surrogate under a Fisher‑dominant ranking condition, the method preserves convergence guarantees while efficiently estimating Fisher scores from squared gradients. Experiments on CIFAR‑10, CIFAR‑100, and AGNews demonstrate that FedFIbOS outperforms state‑of‑the‑art approaches by roughly 10% in accuracy, especially under strong non‑IID heterogeneity.
By Yasmeen Afzal, Jeremiah D. Deng, Haibo Zhang
arXiv:2606. 22775v2 Announce Type: replace-cross Abstract: Distribution shift between training and deployment is a pervasive challenge for modern AI systems.
By Zhewen Hou, Tian Zheng
arXiv:2411.02771v3 Announce Type: replace-cross
Abstract: Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While c...
By Lars van der Laan, Alex Luedtke, Marco Carone
The paper introduces the first federated algorithm for Targeted Maximum Likelihood Estimation (TMLE), enabling hospitals, banks, or registries to perform TMLE without sharing individual data. Two frameworks—FedTMLE‑G, which aggregates local gradients, and FedTMLE‑L, which allows each institution to complete its own fluctuation fit—are presented, along with a finite‑precision communication protocol that keeps numerical targeting error negligible. The authors also discuss privacy implications, convergence bounds, and trade‑offs between institutional influence and sampling variability.
By Diyang Li, Fei Wang, Kyra Gan
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