Collaborative Distributed Machine Learning

Abstract

Various collaborative distributed machine learning (CDML) systems, including federated learning systems and swarm learning systems, with diferent key traits were developed to leverage resources for the development and use of machine learning(ML) models in a conidentiality-preserving way. To meet use case requirements, suitable CDML systems need to be selected. However, comparison between CDML systems to assess their suitability for use cases is often diicult. To support comparison of CDML systems and introduce scientiic and practical audiences to the principal functioning and key traits of CDML systems, this work presents a CDML system conceptualization and CDML archetypes.

References in corpus (13)

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• Decentralized Federated Learning through Proxy Model Sharing
• OpenFL: An open-source framework for Federated Learning
• Decentralized Federated Learning via Mutual Knowledge Transfer
• GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning
• UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning
• NVIDIA FLARE: Federated Learning from Simulation to Real-World
• Confederated Learning: Federated Learning with Decentralized Edge Servers
• DFL: High-Performance Blockchain-Based Federated Learning
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation
• Automated Market Makers in Cryptoeconomic Systems: A Taxonomy and Archetypes
• GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
• SLPerf: a Unified Framework for Benchmarking Split Learning