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:2405. 11667v2 Announce Type: replace Abstract: Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD.
By Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
arXiv:2606. 01128v1 Announce Type: new Abstract: Communication overhead is a crucial bottleneck in scalable distributed learning.
By Tehila Dahan, Bassel Hamoud, Roie Reshef, Martin Jaggi, Kfir Y. Levy
FedLore introduces a communication- and memory-efficient federated learning framework that shares a low-rank optimization basis across clients each round, mitigating subspace fragmentation and enabling exact low-rank aggregation. By refreshing this shared basis across rounds, FedLore allows model updates to exceed the per-round rank budget while maintaining a provable $O(T^{-1/2})$ stationarity bound under standard assumptions. Experiments on vision and language tasks, including federated pre‑training, demonstrate that FedLore outperforms low‑rank adapter baselines and matches or surpasses full‑parameter training while reducing communication and optimizer‑state memory.
By Junkang Liu
arXiv:2409. 19279v2 Announce Type: replace-cross Abstract: Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization.
By Kushal Chakrabarti, Mayank Baranwal
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