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Abstract Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator’s data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant’s privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.

An abstract of the research "Negative regulation of gamma-aminobutyric acid type A receptor on freecalcium ion levels following facial nerve injury," by Fugao Zhu, Dawei Sun, Rui Zhou et al, is presented.

The article discusses a laboratory study wherein muscarinic receptor modulated via different channels the intracellular free calcium ion levels in the facial nerve nucleus following facial nerve injury in rats. It reveals that there was no significant difference in muscarinic receptor expression at the affected facial nerve nucleus compared with expression prior to injury. The article also suggests that the cholinergic M receptor could increase the intracellular free calcium concentration that will promote neuronal regeneration and repair.