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Def-Ag: An energy-efficient decentralized federated learning framework via aggregator clients
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- Authors
- Junyoung Park, Sungpil Woo, Joohyung Lee
- Issue Date
- 2026-02
- Citation
- Future Generation Computer Systems, v.175, pp.1-17
- ISSN
- 0167-739X
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Federated Learning (FL) has revolutionized Artificial Intelligence (AI) by enabling decentralized model training across diverse datasets, thereby addressing privacy concerns. However, traditional FL relies on a centralized server, leading to latency, single-point failures, and trust issues. Decentralized Federated Learning (DFL) emerges as a promising solution, but it faces challenges in achieving optimal accuracy and convergence due to limited client interactions, requiring energy inefficiency. Moreover, balancing the personalization and generalization of the AI model in DFL remains a complex issue. To address those challenging problems, this paper presents Def-Ag, an innovative energy-efficient DFL framework utilizing aggregator clients within similarity-based clusters. To reduce this signaling overhead, a partial model information exchange is proposed in intra-cluster training. In addition, the knowledge distillation method is applied for inter-cluster training to carefully incorporate the knowledge between clusters. Finally, by integrating clustering-based hierarchical DFL and optimizing client selection, Def-Ag reduces energy consumption and communication overhead while balancing personalization and generalization. Extensive experiments on CIFAR-10 and FMNIST datasets confirm Def-Ag's superior performance in reducing energy usage and maintaining learning accuracy compared to baseline methods. The results demonstrate that Def-Ag effectively balances personalization and generalization, providing a robust solution for energy-efficient decentralized federated learning systems.
- KSP Keywords
- CIFAR-10, Communication overhead, Decentralized model, Energy inefficiency, Energy usage, Federated learning, Information exchange, Inter-cluster, Intra-cluster, Knowledge Distillation, Learning framework