Efficiently Distributed Federated Learning

OIAFed: All-in-One Federated Learning Framework

Fedflow: cloud orchestration for federated learning with the FeatureCloud platform

fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud, is introduced, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud that enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics...

FedQS: asynchronous federated learning based on queue scheduling

An asynchronous framework named FedQS, which employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics, and implements a decoupling solution via a queue scheduling algorithm to resolve the coupling...

Federated Learning on the American Science Cloud using APPFL

It is argued that offering federated computing as an important AmSC service would unlock privacy-constrained scientific collaborations, enabling public-private partnerships in model building while exercising and enhancing the platform's own federated infrastructure.

Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment

Flower Hub is presented, a platform for publishing, discovering, and executing decentralized and federated applications that enables reproducible benchmarking by packaging benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows.

Distributed Learning as a Service: The Developer's Perspective

This paper demonstrates DLaaS (Distributed Learning as a Service) from the developer's vantage point and demonstrates the complete service lifecycle on an industrial smart-home Wake-up Word (WuW) task, using the "Ok Aura"dataset.