Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data

• URL: http://arxiv.org/abs/2012.05622v2
• Date: Wed, 3 Feb 2021 13:41:44 GMT
• Title: Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data
• Authors: Naram Mhaisen, Alaa Awad, Amr Mohamed, Aiman Erbad, Mohsen Guizani
• Abstract summary: Federated learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena. In the cases where the participants' data are strongly skewed (i.e., non-IID), the local models can overfit local data, leading to low performing global model. We propose a hierarchical learning system that performs Federated Gradient Descent on the user-edge layer and Federated Averaging on the edge-cloud layer.
• Score: 43.32085029569374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local models' parameters into a global model. Federated learning is a promising paradigm that allows for extending local training among the participant devices before aggregating the parameters, offering better communication efficiency. However, in the cases where the participants' data are strongly skewed (i.e., non-IID), the local models can overfit local data, leading to low performing global model. In this paper, we first show that a major cause of the performance drop is the weighted distance between the distribution over classes on users' devices and the global distribution. Then, to face this challenge, we leverage the edge computing paradigm to design a hierarchical learning system that performs Federated Gradient Descent on the user-edge layer and Federated Averaging on the edge-cloud layer. In this hierarchical architecture, we formalize and optimize this user-edge assignment problem such that edge-level data distributions turn to be similar (i.e., close to IID), which enhances the Federated Averaging performance. Our experiments on multiple real-world datasets show that the proposed optimized assignment is tractable and leads to faster convergence of models towards a better accuracy value.

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