distributed optimization

BIQ: Bisection Interval Quantization for Communication-efficient Federated Learning AAAI 2026 First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints UAI 2026 Learning to accelerate distributed ADMM using graph neural networks L4DC 2026 Markovian Compression: Looking to the Past Helps Accelerate the Future UAI 2026 Ordered Local Momentum for Asynchronous Distributed Learning Under Arbitrary Delays AAAI 2026 Parallelizable Riemannian Alternating Direction Method of Multipliers for Non-convex Pose Graph Optimization AAAI 2026 Quantized Stochastic Primal–Dual Methods for Distributed Optimization under Relaxed Global Geometry UAI 2026 S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems AAAI 2026 Accelerated Methods with Compressed Communications for Distributed Optimization Problems Under Data Similarity AAAI 2025 A Primal-dual Perspective for Distributed TD-learning IJCAI 2025 Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization AISTATS 2025 Connecting Federated ADMM to Bayes ICLR 2025 Correlated Quantization for Faster Nonconvex Distributed Optimization UAI 2025 Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration JMLR 2025 Decentralized Sparse Linear Regression via Gradient-Tracking JMLR 2025 Deep Distributed Optimization for Large-Scale Quadratic Programming ICLR 2025 Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis JMLR 2025 EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback JMLR 2025 EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity AAAI 2025 FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning IJCAI 2025 FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning AAAI 2025 Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence AAAI 2025 Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing ICLR 2025 PA3Fed: Period-Aware Adaptive Aggregation for Improved Federated Learning AAAI 2025 Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis AISTATS 2025