CAEFL: composable and environment aware federated learning models

Xu, R. (C.), Michala, A. L. and Trinder, P. (2022) CAEFL: composable and environment aware federated learning models. In: Erlang 2022: Proceedings of the 21st ACM SIGPLAN International Workshop on Erlang, Ljubljana, Slovenia, 11-16 Sep 2022, pp. 9-20. ISBN 9781450394352 (doi: 10.1145/3546186.3549927)

Abstract

Federated Learning allows multiple distributed agents to contribute to a global machine learning model. Each agent trains locally and contributes to a global model by sending gradients to a central parameter server. The approach has some limitations: 1) some events may only occur in the local environment, so a global model may not perform as well as a specialized model; 2) changes in the local environment may require an agent to use some dedicated model, that is not available in a single global model; 3) a single global model approach is unable to derive new models from dealing with complex environments. This paper proposes a novel federated learning approach, CAEFL, that is local environment aware and composes new dedicated models for new complex environments. CAEFL is implemented in Elixir to exploit transparent distribution, pattern matching, and hot-code-swapping. Pattern matching is used to transform environment sensors data to corresponding tags and aggregate data with the same environment tags on agents. It is also used on parameter server to match client’s push/pull request for these tagged models. It enables a declarative way for environment aware federated learning approach. CAEFL outperforms state of the art federated learning by 7-10% for the MNIST dataset and 2% for the FashionMNIST dataset in specific and complex environments.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: XU, Cocoa and Trinder, Professor Phil and Michala, Dr Lito
• Authors: Xu, R. (C.), Michala, A. L., and Trinder, P.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450394352
• Published Online: 06 September 2022
• Copyright Holders: Copyright © 2022 Association for Computing Machinery
• First Published: First published in Erlang 2022: Proceedings of the 21st ACM SIGPLAN International Workshop on Erlang: 9-20
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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