all AI news
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
April 23, 2024, 4:43 a.m. | Mohak Chadha, Alexander Jensen, Jianfeng Gu, Osama Abboud, Michael Gerndt
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, current serverless FL systems still suffer from the presence of …
abstract arxiv as-a-service collaborative computing cs.dc cs.lg data decentralized designing distributed enabling environments federated learning function global machine machine learning paradigm serverless serverless computing service systems technologies training type
More from arxiv.org / cs.LG updates on arXiv.org
Efficient Data-Driven MPC for Demand Response of Commercial Buildings
2 days, 22 hours ago |
arxiv.org
Testing the Segment Anything Model on radiology data
2 days, 22 hours ago |
arxiv.org
Calorimeter shower superresolution
2 days, 22 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US