Federated Learning: A decentralized approach to smarter, safer AI

Atos FL framework is a Python library that allows defining computational graphs to implement federated learning tasks. It is based on a pipes and filters design pattern to model the federated actors, the operations they perform and the exchange of information between them. Its main features are: modularity, interoperability and customizability (as it defines a modular architecture based on a set of functional units that can be inherited and extended, allowing new components to be implemented or custom functions to be injected to fully personalize the federated pipeline); strong privacy-preserving and secure aggregation mechanisms (by supporting several privacy-preserving mechanisms and multiple advanced secure aggregation algorithms); flexibility and adaptability (by allowing the implementation of complex topologies, such as swarm learning or hierarchical learning); efficiency and scalability (by integrating model compression capabilities including both lossless and lossy techniques to reduce communication overhead); and third-party integration (by offering seamless integration with mainstream ML tools and MLOps frameworks).