Federated Learning using Peer-to-peer Network for Decentralized Orchestration of Model Weights

In recent times, Machine learning and Artificial intelligence have become one of the key emerging fields of computer science. Many researchers and businesses are benefited by machine learning models that are trained by data processing at scale. However, machine learning, and particularly Deep Learning requires large amounts of data, that in several instances are proprietary and confidential to many businesses. In order to respect individual organization’s privacy in collaborative machine learning, federated learning could play a crucial role. Such implementations of privacy preserving federated learning find applicability in various ecosystems like finance, health care, legal, research and other fields that require preservation of privacy. However, many such implementations are driven by a centralized architecture in the network, where the aggregator node becomes the single point of failure, and is also expected with lots of computing resources at its disposal. In this paper, we propose an approach of implementing a decentralized, peer-topeer federated learning framework, that leverages RAFT based aggregator selection. The proposal hinges on that fact that there is no one permanent aggregator, but instead a transient, time based elected leader, which will aggregate the models from all the peers in the network. The leader ( aggregator) publishes the aggregated model on the network, for everyone to consume. Along with peer-to-peer network and RAFT based aggregator selection, the framework uses dynamic generation of cryptographic keys, to create a more secure mechanism for delivery of models within the network. The key rotation also ensures anonymity of the sender on the network too. Experiments conducted in the paper, verifies the usage of peer-to-peer network for creating a resilient federated learning network. Although the proposed solution uses an artificial neural network in it’s reference implementation, the generic design of the framework can accommodate any federated learning model within the network.