Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks

Naman, P and Simmhan, Y (2024) Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks. In: 30th International Conference on Parallel and Distributed Computing, Euro-Par 2024, 26 August 2024 through 30 August 2024, Madrid, pp. 470-484.

PDF Lec_Not_Com_Sci_2024.pdf - Published Version Restricted to Registered users only Download (963kB) | Request a copy

Abstract

Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, addressing privacy concerns while leveraging parallelism. Existing methods that address the unique requirements of federated GNN training using remote embeddings to enhance convergence accuracy are limited by their diminished performance due to large communication costs with a shared embedding server. In this paper, we present OpES, an optimized federated GNN training framework that uses remote neighbourhood pruning, and overlaps pushing of embeddings to the server with local training to reduce the network costs and training time. The modest drop in per-round accuracy due to pre-emptive push of embeddings is out-stripped by the reduction in per-round training time for large and dense graphs like Reddit and Products, converging up to �2� faster than the state-of-the-art technique using an embedding server and giving up to 20 better accuracy than vanilla federated GNN learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Item Type:	Conference Proceedings
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords:	Adversarial machine learning; Contrastive Learning; Differential privacy; Graph embeddings; Graph neural networks; Network embeddings, Decentralised; Embeddings; Graph data; Graph neural networks; Learn+; Machine learning approaches; Neural networks trainings; Privacy concerns; Shared model; Training time, Federated learning
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	12 Sep 2024 06:57
Last Modified:	12 Sep 2024 06:57
URI:	http://eprints.iisc.ac.in/id/eprint/86125

Actions (login required)

View Item