A Framework for testing Federated Learning algorithms using an edge-like environment

Abstract

Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer workloads (both hardware and software) as close as possible to the edge, where the data is being created and where actions are occurring, enabling faster response times, greater data privacy, and reduced data transfer costs. However, due to the heterogeneous data distributions/contents of clients, it is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation. This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance. In general, testing and assessing FL algorithms can be a very difficult and complex task due to the distributed nature of the systems. In this work, a framework is proposed and implemented to assess FL algorithms in a more easy and scalable way. This framework is evaluated over a distributed edge-like environment managed by a container orchestration platform (i.e. Kubernetes).

References in corpus (7)

• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Federated Learning for Mobile Keyboard Prediction
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
• FedML: A Research Library and Benchmark for Federated Machine Learning
• A generic framework for privacy preserving deep learning
• DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning
• Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT