Reliable Federated Learning for Mobile Networks

By Jiawen Kang, Zehui Xiong, Dusist Niyato, Y. Zou, Yang Zhang, Mohsen Guizani. IEEE wireless communications, 2019

561 citations recorded by the source corpus.

Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, for example, the data poisoning attack, or unintentionally, for example, low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/618babd71f257f063d6ad2ecf1b2070d34721dda

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Citations in the corpus

Federated Learning for Smart Healthcare: A Survey (2021, background): “To address these challenges, blockchain is adopted in [51] to manage FL users’ reputation.”
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, background): “This in turn endows on the devices human-like intelligence to respond to real-time events [4]–[9].”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “The study in [145,147] uses blockchain and multi-weight subjective logic to formulate reputation scores; the reputation is calculated based on previous interactions and opinions of other task publishers.”
Federated learning for 6G communications: Challenges, methods, and future directions (2020, background): “Kang et al. in [12] proposed a reputation management scheme to calculate the historical reputation of the devices to achieve a robust FL with high-reputation devices.”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “To address this problem, the authors in [320] propose a secure FL system”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, background): “[74] introduce the concept of reputation based on a consortium blockchain.”
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach (2020, background): “Based on the above empirical results, the following observations can be derived: a challenge: the local data are not i.i.d. [52], [53].”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “…values indicating the relative strength of the respective FL approach in that domain. metrics, which are the focal points of current state-of-the-art (SOTA) research and worthy of further investigation [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “implement edge artificial intelligence (AI) in B5G networks [8], [9], [10], [11], [12], [13].”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “In wireless communications, implementation of FL has the following advantages [15,22]: ① exchanging local machine learning model parameters instead of voluminous training data can save energy and consume less wireless resources; ② training machine learning model parameters locally can effectively…”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, unknown): “The authors of [125]”