Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection

• URL: http://arxiv.org/abs/2410.17792v1
• Date: Wed, 23 Oct 2024 11:47:04 GMT
• Title: Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection
• Authors: Charuka Herath, Xiaolan Liu, Sangarapillai Lambotharan, Yogachandran Rahulamathavan,
• Abstract summary: Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. We present a method to improve convergence in FL by creating a global subset of data on the server and dynamically distributing it across devices. Our approach results in a substantial accuracy boost of approximately 5% for the MNIST dataset, around 18% for CIFAR-10, and 20% for CIFAR-100 with a 10% global subset of data, outperforming the state-of-the-art (SOTA) aggregation algorithms.
• Score: 13.825031686864559
• License:
• Abstract: Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, security, regulatory compliance, and cost-efficiency. Our emphasis in this research lies in addressing statistical complexity in FL, especially when the data stored locally across devices is not identically and independently distributed (non-IID). We have observed an accuracy reduction of up to approximately 10\% to 30\%, particularly in skewed scenarios where each edge device trains with only 1 class of data. This reduction is attributed to weight divergence, quantified using the Euclidean distance between device-level class distributions and the population distribution, resulting in a bias term (\(\delta_k\)). As a solution, we present a method to improve convergence in FL by creating a global subset of data on the server and dynamically distributing it across devices using a Dynamic Data queue-driven Federated Learning (DDFL). Next, we leverage Data Entropy metrics to observe the process during each training round and enable reasonable device selection for aggregation. Furthermore, we provide a convergence analysis of our proposed DDFL to justify their viability in practical FL scenarios, aiming for better device selection, a non-sub-optimal global model, and faster convergence. We observe that our approach results in a substantial accuracy boost of approximately 5\% for the MNIST dataset, around 18\% for CIFAR-10, and 20\% for CIFAR-100 with a 10\% global subset of data, outperforming the state-of-the-art (SOTA) aggregation algorithms.

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