LayerCFL: an efficient federated learning with layer-wised clustering

• Published in: Cybersecurity (Singapore) Vol. 6; no. 1; pp. 39 - 14
• Main Authors: Yuan, Jie, Qian, Rui, Yuan, Tingting, Sun, Mingliang, Li, Jirui, Li, Xiaoyong
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2023; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Clients; Clustered federated learning; Clustering; Computer Applications; Computer Science; Cybercrime; Cybersecurity; Federated learning; Internet; Layer-wised clustering; Medical research; Neural networks; Non-IID; Research methodology; Clustered federated learning; Federated learning; Layer-wised clustering; Non-IID
• DOI: 10.1186/s42400-023-00172-x
• ISSN: 2523-3246

Federated Learning (FL) suffers from the Non-IID problem in practice, which poses a challenge for efficient and accurate model training. To address this challenge, prior research has introduced clustered FL (CFL), which involves clustering clients and training them separately. Despite its potential benefits, CFL can be computationally and communicationally expensive when the data distribution is unknown beforehand. This is because CFL involves the entire neural networks of involved clients in computing the clusters during training, which can become increasingly time-consuming with large-sized models. To tackle this issue, this paper proposes an efficient CFL approach called LayerCFL that employs a Layer-wised clustering technique. In LayerCFL, clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental methods. Our experimental results demonstrate the effectiveness of LayerCFL in mitigating the impact of Non-IID data, improving the accuracy of clustering, and enhancing computational efficiency.