Personalized Federated Learning Method Based on Attention-Enhanced Meta-Learning Network

Gao Yujia; Wang Pengfei; Liu Liang; Ma Huadong

doi:10.7544/issn1000-1239.202220922

Journal of Computer Research and Development > 2024 > 61(1): 196-208. > DOI: 10.7544/issn1000-1239.202220922 CSTR: 32373.14.issn1000-1239.202220922

Gao Yujia, Wang Pengfei, Liu Liang, Ma Huadong. Personalized Federated Learning Method Based on Attention-Enhanced Meta-Learning Network[J]. Journal of Computer Research and Development, 2024, 61(1): 196-208. DOI: 10.7544/issn1000-1239.202220922

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Personalized Federated Learning Method Based on Attention-Enhanced Meta-Learning Network

Gao Yujia^{1, 2,},
Wang Pengfei¹,
Liu Liang^{2, 3, ,},
Ma Huadong^{1, 2}

1.
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876
2.
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunications), Beijing 100876
3.
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876

Funds: This work was supported by the National Natural Science Foundation of China (61932013, 62061146002, 62225204) and the National 111 Project (B18008).

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Author Bio:
Gao Yujia: born in 1995. PhD. Her main research interests include federated learning, deep learning, and intelligent sensing

Wang Pengfei: born in 1989. PhD, associate professor. His main research interests include data mining, machine learning, and recommendation system

Liu Liang: born in 1982. PhD, professor. Senior member of CCF. His main research interests include Internet of things and intelligence sensing

Ma Huadong: born in 1964. PhD, professor, fellow of CCF, fellow of IEEE. His main research interests include Internet of things, sensor networks, multimedia system and networking
Received Date: November 02, 2022
Revised Date: March 21, 2023
Available Online: January 07, 2024

Graphical Abstract

Abstract

Abstract

Federated learning is a distributed machine learning framework, which enables clients to conduct model training without transmitting their data to the servers. It is used to solve the dilemma of data silos and data privacy. It can work well on clients with similar data characteristics and distribution. However, in many scenarios, the differences of data distribution cause difficulties in global model training. Therefore, personalized federated learning is proposed as a new federated learning paradigm. It aims to guarantee the effectiveness of client personalized models through the collaboration between clients and the servers. Intuitively, providing a tighter collaboration for clients with similar data characteristics and distribution can facilitate the construction of personalized models. However, due to the invisibility of client data, it is a challenge to extract client features at a fine-grained level and define the collaborative relationships between clients. In this paper, we design an attention-enhanced meta-learning network (AMN) to address this issue. AMN can utilize model parameters as features and train the meta-learning network to provide an additional meta-model for each client to automatically analyze client feature similarity. According to two-layers framework of AMN, a trade-off between clients’ personality and commonality can be reasonably achieved, and a hybrid model with useful information from all clients is provided. Considering the need to maintain both the meta-model and the client’s local model during the training process, we design an alternative training strategy to perform the training in an end-to-end manner. To demonstrate the effectiveness of our method, we conduct extensive experiments on two benchmark datasets and eight baseline methods. Compared with the existing best-performing personalized federated learning methods, our method improves the accuracy rate by 3.39% and 2.45% on average in two datasets.
- federated learning,
- attention mechanism,
- deep learning,
- meta-learning,
- distributed machine learning

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References (33)

References

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