3 papers
math.OC2026
AdGT: Decentralized Gradient Tracking with Tuning-free Per-Agent Stepsize
Diyako Ghaderyan, Stefan Werner
In decentralized optimization, the choice of stepsize plays a critical role in algorithm performance. A common approach is to use a shared stepsize across all agents to ensure conv…
cs.LG2025
Federated Smoothing ADMM for Localization
Reza Mirzaeifard, Ashkan Moradi, Masahiro Yukawa +1
This paper addresses the challenge of localization in federated settings, which are characterized by distributed data, non-convexity, and non-smoothness. To tackle the scalability…
cs.LG2025
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning
Reza Mirzaeifard, Stefan Werner
This paper presents a hierarchical federated learning (FL) framework that extends the alternating direction method of multipliers (ADMM) with smoothing techniques, tailored for non…