Federated Nonparametric Regression with Heterogeneous Clients
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
The Flow has not summarised this story yet — read it at arXiv Statistics ML.
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.
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.
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...
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.
arXiv:2608. 09074v1 Announce Type: cross Abstract: We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB).