Privacy and Efficiency of Communications in Federated Split Learning. (arXiv:2301.01824v1 [cs.LG])

Everyday, large amounts of sensitive data \sai{is} distributed across mobile
phones, wearable devices, and other sensors. Traditionally, these enormous
datasets have been processed on a single system, with complex models being
trained to make valuable predictions. Distributed machine learning techniques
such as Federated and Split Learning have recently been developed to protect
user \sai{data and} privacy better while ensuring high performance. Both of
these distributed learning architectures have advantages and disadvantages. In
this paper, we examine these tradeoffs and suggest …

communications data datasets devices distributed efficiency high large machine machine learning mobile mobile phones performance phones predictions privacy protect sai sensitive data sensors single split learning system techniques wearable wearable devices