all AI news
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains
March 12, 2024, 4:43 a.m. | Nikita Tsoy, Anna Mihalkova, Teodora Todorova, Nikola Konstantinov
cs.LG updates on arXiv.org arxiv.org
Abstract: Cross-silo federated learning (FL) allows data owners to train accurate machine learning models by benefiting from each others private datasets. Unfortunately, the model accuracy benefits of collaboration are often undermined by privacy defenses. Therefore, to incentivize client participation in privacy-sensitive domains, a FL protocol should strike a delicate balance between privacy guarantees and end-model accuracy. In this paper, we study the question of when and how a server could design a FL protocol provably beneficial …
abstract accuracy arxiv benefits client collaboration cs.cr cs.gt cs.lg data datasets domains federated learning machine machine learning machine learning models model accuracy privacy protocol stat.ml strike train type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US