Devising an actor-based middleware support to federated learning experiments and systems

Federated Learning (FL) recently emerged as a practical privacy-preserving paradigm to exploit data distributed over separated repositories for Machine Learning purposes, with no need to migrate data. FL algorithms entail concerted activities of multiple distributed players: a dedicated supporting system aims to relieve programmers from dealing with the intricate implementation details of communication and synchronization activities required along the distributed model learning, and the necessary information exchange during operation. Such support plays a crucial role in the experimentation of FL algorithms and their eventual field operation, so its architecture must be carefully designed. In this work, we propose a novel architecture where the pivotal role is assigned to a runtime system based on actors, working at the middleware level. The distinctive points of this approach are portability across diverse platforms, location transparency for the involved nodes, opportunity to choose diverse languages for implementing the core parts of custom software systems. Moreover, with the proposed solution, scalability requirements can be easily met. The implementation of FL algorithms is made easier by APIs to programmatically access the middleware functionalities. Another benefit is that the same code can be used in both simulated and Fed-lang, the reference implementation of the proposed architecture, has been used to quantitatively compare the characteristics of our approach with other existing FL frameworks, showing its ability to address the challenges posed by various operating conditions and settings. The described architecture has shown to be adequate to deliver the functionalities required for the effective development of FL systems.