FedILC
PublicOur proposed FedILC algorithm leverages the gradient covariance and weighted geometric mean of Hessians to capture both inter-silo and intra-silo consistencies of environments and unravel the domain shift problems in federated networks
Comprehensive AI Models Collection for All Your Development & Research Needs
AI LLM Power Rankings - Performance, Buzz & Trends
Discover Trusted AI Model Partners - Guaranteed Reliable Support
Submit Your Model Info & Services - Precision Marketing & User Targeting
Discover Popular AI-MCP Services - Find Your Perfect Match Instantly
Easy MCP Client Integration - Access Powerful AI Capabilities
Master MCP Usage - From Beginner to Expert
Top MCP Service Performance Rankings - Find Your Best Choice
Publish & Promote Your MCP Services
Large-scale datasets and benchmarks for training, evaluating, and testing models to measure
Comprehensive Text Extraction and Document Processing Solutions for Users
Our proposed FedILC algorithm leverages the gradient covariance and weighted geometric mean of Hessians to capture both inter-silo and intra-silo consistencies of environments and unravel the domain shift problems in federated networks