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.
By M Yashwanth, Sharannya Ghosh, Saikiran Bulusu, Anirban Chakraborty
arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.
By Shiyuan Zuo, Jiashuo Li, Rongfei Fan, Han Hu, Jie Xu
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
arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.
By Grigory Malinovsky
arXiv:2603. 05774v2 Announce Type: replace Abstract: This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints.
By Zhankun Luo, Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi
arXiv:2603. 19617v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient framework for solving distributed optimization problems.
By Mohammadjavad Ebrahimi, Daniel Burbano, Farzad Yousefian
The paper introduces Batched SGD, a variant that groups online samples into epochs and performs a single update per epoch using a low‑variance gradient estimate. This batching approach allows a straightforward high‑probability analysis without restrictive assumptions or auxiliary sequences, yielding near‑optimal rates for both strongly convex and non‑convex objectives under standard smoothness and sub‑Gaussian noise conditions. The authors also extend the method to federated learning, providing the first high‑probability guarantees with logarithmic communication complexity, linear speedup in the number of agents, and robustness to data heterogeneity.
By Feng Zhu, Robert W. Heath Jr., Aritra Mitra
arXiv:2607. 14731v1 Announce Type: new Abstract: Local SGD, also known as Federated Averaging, is a widely used distributed optimization algorithm.
By Kumar Kshitij Patel, Rustem Islamov, Sebastian U Stich, Aurelien Lucchi, Eduard Gorbunov, Lingxiao Wang
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
The paper introduces DUCB-OGD, an algorithm that couples a Discounted Upper‑Confidence‑Bound sampler with Online Gradient Descent to address dynamic minimax regret in robust large‑language‑model post‑training. It operates under instantaneous mini‑batch‑only bandit feedback, tracking worst‑source performance without re‑evaluating historical data. Experiments on fine‑tuning, preference optimization, and reinforcement learning demonstrate that DUCB‑OGD improves worst‑group robustness with negligible computational overhead.
arXiv:2604. 24012v3 Announce Type: replace Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments.
By Yutong He, Zhengyang Huang, Jiahe Geng, Kun Yuan
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