Field-Based Coordination for Federated Learning

TY - GEN

T1 - Field-Based Coordination for Federated Learning

AU - Domini, Davide

AU - Aguzzi, Gianluca

AU - Esterle, Lukas

AU - Viroli, Mirko

N1 - Publisher Copyright: © IFIP International Federation for Information Processing 2024.

PY - 2024

Y1 - 2024

N2 - Federated Learning has gained increasing interest in the last years, as it allows the training of machine learning models with a large number of devices by exchanging only the weights of the trained neural networks. Without the need to upload the training data to a central server, privacy concerns and potential bottlenecks can be removed as fewer data is transmitted. However, the current state-of-the-art solutions are typically centralized, and do not provide for suitable coordination mechanisms to take into account spatial distribution of devices and local communications, which can sometimes play a crucial role. Therefore, we propose a field-based coordination approach for federated learning, where the devices coordinate with each other through the use of computational fields. We show that this approach can be used to train models in a completely peer-to-peer fashion. Additionally, our approach also allows for emergently create zones of interests, and produce specialized models for each zone enabling each agent to refine their models for the tasks at hand. We evaluate our approach in a simulated environment leveraging aggregate computing—the reference global-to-local field-based coordination programming paradigm. The results show that our approach is comparable to the state-of-the-art centralized solutions, while enabling a more flexible and scalable approach to federated learning.

AB - Federated Learning has gained increasing interest in the last years, as it allows the training of machine learning models with a large number of devices by exchanging only the weights of the trained neural networks. Without the need to upload the training data to a central server, privacy concerns and potential bottlenecks can be removed as fewer data is transmitted. However, the current state-of-the-art solutions are typically centralized, and do not provide for suitable coordination mechanisms to take into account spatial distribution of devices and local communications, which can sometimes play a crucial role. Therefore, we propose a field-based coordination approach for federated learning, where the devices coordinate with each other through the use of computational fields. We show that this approach can be used to train models in a completely peer-to-peer fashion. Additionally, our approach also allows for emergently create zones of interests, and produce specialized models for each zone enabling each agent to refine their models for the tasks at hand. We evaluate our approach in a simulated environment leveraging aggregate computing—the reference global-to-local field-based coordination programming paradigm. The results show that our approach is comparable to the state-of-the-art centralized solutions, while enabling a more flexible and scalable approach to federated learning.

KW - Aggregate computing

KW - Federated learning

KW - Field-based coordination

UR - http://www.scopus.com/inward/record.url?scp=85197231208&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-62697-5_4

DO - 10.1007/978-3-031-62697-5_4

M3 - Article in proceedings

AN - SCOPUS:85197231208

SN - 9783031626968

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 56

EP - 74

BT - Coordination Models and Languages - 26th IFIP WG 6.1 International Conference, COORDINATION 2024, Held as Part of the 19th International Federated Conference on Distributed Computing Techniques, DisCoTec 2024, Proceedings

A2 - Castellani, Ilaria

A2 - Tiezzi, Francesco

PB - Springer Science and Business Media Deutschland GmbH

T2 - 26th International Conference on Coordination Models and Languages, COORDINATION 2024

Y2 - 18 June 2024 through 20 June 2024

ER -