arXiv:2610. 35663v1 Announce Type: new Abstract: One of the main challenges of federated learning is heterogeneous data split among the clients, leading to biased local updates called client drift.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 14731v1 Announce Type: new Abstract: Local SGD, also known as Federated Averaging, is a widely used distributed optimization algorithm.
arXiv:2310. 15976v4 Announce Type: replace Abstract: signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients.
arXiv:2610.33835v1 Announce Type: cross Abstract: What is the round complexity of Hessian-similar optimization when each round can contact at most $s$ of $m$ clients? Prior rates permit a free master...
arXiv:2505.20817v3 Announce Type: replace-cross Abstract: Gradient clipping is widely used in language-model training to control heavy-tailed gradient noise and can improve convergence guarantees ove...
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...
The paper introduces a federated minimax framework where the minimization variable is shared across clients while each client retains a personalized maximization variable. It proposes a projected ascent method for the personalized variables and shows that in nonconvex-concave settings it achieves a sublinear convergence rate, but in nonconvex-PL settings it suffers from a noise floor. To overcome this, the authors develop VR-SGDA, a variance‑reduced algorithm that eliminates the noise floor through a two‑timescale update scheme, and demonstrate its effectiveness on subpopulation‑robustness and NLP adversarial training tasks.