IEEE

Mixture of Specialized Experts for Model-Heterogeneous Personalized Federated Learning

Abstract: Federated learning is a distributed machine learning paradigm designed to facilitate collaborative model training while preserving user data privacy. However, in practical scenarios, data ...

Gaggle Helps School Leaders Stay Ahead of AI Risks with Real-Time Web Filtering

Gaggle’s Real-Time Web Filter Keeps Students Safe by Blocking Harmful Content Accessed Through AI Search Mode Today’s ...
IEEE

Edge-PRUNE: A Dataflow-Based Framework for Distributed Signal Processing and Machine Learning

Abstract: Distributed sensing through video, audio, radar and other sensors is strongly growing with application areas such as smart homes and Internet of Things. The concept of edge computing ...
marktechpost

Microsoft AI Proposes OrbitalBrain: Enabling Distributed Machine Learning in Space with Inter-Satellite Links and Constellation-Aware Resource Optimization Strategies

Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main ...
marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...