FedKTL

You only need to configure one file to support model heterogeneity. Consistent GPU memory usage for single or multiple clients.

AAAI 2024 accepted paper, FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning

[ICML2022] ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

The implementation of "Two-Stream Federated Learning: Reduce the Communication Costs" (VCIP 2018)

FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.

Code for the paper "A Quadratic Synchronization Rule for Distributed Deep Learning"

Template-based federated multiview domain alignment

Request Management System