Jupiter: a modern federated learning platform for regional medical care

Xing, Ju; Tian, Jiadong; Jiang, Zexun; Cheng, Jiali; Yin, Hao

doi:10.1007/s11432-020-3062-8

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…( 2) DNN (w.r.t. non-convex case): We adopt SVHN 3 and CIFAR10 4 datasets to examine the theoretic findings subject to non-convex objective functions (Theorems 2 and 4). According to the above description, we likewise perform the data allocation on these two datasets for acquiring non-iid data distribution.…”

Section: Methodsmentioning

confidence: 99%

Differentially Private Federated Learning on Non-iid Data: Convergence Analysis and Adaptive Optimization

Chen,

Ding,

Bao

et al. 2024

IEEE Trans. Knowl. Data Eng.

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Federated learning (FL) has attracted increasing attention in recent years due to its data privacy preservation and great applicability to large-scale user scenarios. However, when FL faces numerous clients, it is inevitable to emerge the non-independent and identically distributed (non-iid) data between clients, which brings an enormous challenge for model training and performance analysis like convergence. Besides, due to the non-iid data, the participating clients of FL tend to be extremely heterogeneous so the number of samplings among clients causes a sampling variance problem, which induces a huge variation in convergence. More importantly, although FL can foster privacy security via locally retaining the training data, if local data is secret and sensitive, FL should have more powerful privacy protection to resist the cloud server or third party to infer private information from shared models or intermediate gradients. Facing the non-iid and privacy challenges, we propose a differential privacy (DP) based non-iid FL algorithm called DPNFL to jointly tackle these two issues. Specifically, motivated by the DP and its variants, we are the first to adopt the truncated concentrated differential privacy technique under the FL scenario to more tightly track end-to-end privacy loss, while requiring less noise injection for the same level of DP. To avoid the sampling variance problem, we enable the server to sample the partial clients uniformly without replacement, which also guarantees unbiased sampling. To further improve the algorithm performance, we also propose an adaptive version of DPNFL named AdDPNFL, which adopts the adaptive optimization on the server-side to simultaneously alleviate the impact of non-iid data and DP noise on model utility. Finally, we perform extensive experiments to validate the effectiveness and superiority of our algorithms.

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Section: Methodsmentioning

confidence: 99%

Differentially Private Federated Learning on Non-iid Data: Convergence Analysis and Adaptive Optimization

Chen,

Ding,

Bao

et al. 2024

IEEE Trans. Knowl. Data Eng.

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show abstract

“…In healthcare, for instance, Akter et al [26] introduced a privacy-preserving framework based on FL to safeguard healthcare systems against privacy infringements at the edge. Similarly, Xing et al [27] built a federated machine learning platform to enable sharing of medical data while protecting data privacy. For autonomous driving, Fu et al [28] developed a federated reinforcement learning approach specifically designed to alleviate the issues of communication load and privacy concerns inherent in the transmission of raw data.…”

Section: Federated Learningmentioning

confidence: 99%

Towards Reliable Federated Learning Using Blockchain-Based Reverse Auctions and Reputation Incentives

Ouyang,

Yu,

Cao

et al. 2023

Symmetry

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In recent years, the explosion of big data has presented unparalleled opportunities for the advancement of machine learning (ML). However, the vast size and sensitive nature of these datasets present significant challenges in terms of privacy and security. Federated Learning has emerged as a promising solution that enables a group of participants to train ML models without compromising the confidentiality of their raw data. Despite its potential, traditional federated learning faces challenges such as the absence of participant incentives and audit mechanisms. Furthermore, these challenges become more significant when dealing with the scale and diversity of big data, making efficient and reliable federated learning a complex task. These limitations may compromise model quality due to potential malicious nodes. To address the above issues, this paper proposes a BlockChain-based Decentralized Federated Learning (BCD-FL) model. In BCD-FL, we design a smart contract approach based on the reverse auction-based incentive mechanism and a reputation mechanism to promote the participation of reliable and high-quality data owners. Theoretical analysis shows that the BCD-FL model satisfies several desirable properties, such as individual rationality, computational efficiency, budget balance, and truthfulness. In addition, experimental results also show that the proposed model enables more efficient federated learning and provides some level of protection against malicious nodes. Therefore, the BCD-FL model presents a potential solution to the challenges in federated learning and opens up new possibilities for achieving efficient large-scale machine learning.

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ACCEL: an efficient and privacy-preserving federated logistic regression scheme over vertically partitioned data

Zhao

Zhu

Wang

et al. 2022

Sci. China Inf. Sci.

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Jupiter: a modern federated learning platform for regional medical care

Cited by 7 publications

References 24 publications

Differentially Private Federated Learning on Non-iid Data: Convergence Analysis and Adaptive Optimization

Differentially Private Federated Learning on Non-iid Data: Convergence Analysis and Adaptive Optimization

Towards Reliable Federated Learning Using Blockchain-Based Reverse Auctions and Reputation Incentives

ACCEL: an efficient and privacy-preserving federated logistic regression scheme over vertically partitioned data

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