System-Design-for-Federated-Learning

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

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

The communication efficiency of federated learning is improved by sparsifying the parameters uploaded by the clients.

基于代码本迁移的高效联邦学习

This project implements communication-efficient online federated learning strategies for resource-constrained devices using random Fourier features (RFFs) for kernel regression. It reduces communication overhead through partial-sharing and event-triggered updates, ensuring efficient local model updates.