Design and Implementation of a Federated Learning Framework with End-Edge-Cloud Collaboration

Abstract

Abstract: With the increasing demand for data privacy protection, federated learning has emerged as a crucial method for safeguarding data privacy due to its ability to enable joint model training without sharing raw data. However, federated learning still faces numerous challenges in practical applications, such as poisoning attacks by malicious clients, high communication costs, and excessive computational load on central servers. To address these issues, a federated learning framework with end-edge-cloud collaboration is proposed. By hierarchically managing clients, edge servers, and cloud servers, this framework effectively reduces the communication burden on cloud servers while improving system scalability and efficiency. Additionally, a federated learning algorithm was designed
within this framework, combining reputation-based local model aggregation and fully homomorphic encryption-based global model ag⁃
gregation to effectively counteract data poisoning attacks. Experimental results demonstrate that the federated learning framework with end-edge-cloud collaboration not only enhances the system's defense against malicious attacks but also significantly reduces communication overhead and improves system efficiency.

Key words: federated learning, reputation mechanism, fully homomorphic encryption, data poisoning attack

CLC Number:

TP311

Yang Yuelang, Wang Xinchen, Yang Yiyu, Yang Dengqi, Li Xiaowei. Design and Implementation of a Federated Learning Framework with End-Edge-Cloud Collaboration[J]. Journal of Dali University, 2026, 11(6): 19-26.

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