all AI news
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
Feb. 27, 2024, 5:43 a.m. | Xinpeng Ling, Jie Fu, Kuncan Wang, Haitao Liu, Zhili Chen
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data. However, adversaries can still infer individual information through inference attacks (e.g. differential attacks) on these training parameters. As a result, Differential Privacy (DP) has been widely used in FL to prevent such attacks.
We consider differentially private federated learning in a resource-constrained scenario, where both privacy budget and communication …
abstract ali arxiv attacks cs.cr cs.lg data devices differential distributed federated learning inference information machine machine learning multiple organizations parameters raw through training type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
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
AI Research Scientist
@ Vara | Berlin, Germany and Remote