Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

To enable large-scale machine learning in bandwidth-hungry environments such as
wireless networks, significant progress has been made recently in designing communication …

Data heterogeneity across clients is a key challenge in federated learning. Prior works
address this by either aligning client and server models or using control variates to correct …

First proposed by Seide (2014) as a heuristic, error feedback (EF) is a very popular
mechanism for enforcing convergence of distributed gradient-based optimization methods …

We study the problem of distributed stochastic non-convex optimization with intermittent
communication. We consider the full participation setting where $ M $ machines work in …

Communication efficiency has been widely recognized as the bottleneck for large-scale
decentralized machine learning applications in multi-agent or federated environments. To …

Vertical federated learning (VFL), where data features are stored in multiple parties
distributively, is an important area in machine learning. However, the communication …

Due to the high communication cost in distributed and federated learning, methods relying
on compressed communication are becoming increasingly popular. Besides, the best …

Purpose This study aims to summarize the critical issues in medical federated learning and
applicable solutions. Also, detailed explanations of how federated learning techniques can …

We propose and analyze several stochastic gradient algorithms for finding stationary points
or local minimum in nonconvex, possibly with nonsmooth regularizer, finite-sum and online …