LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data

By Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, Dianbo Liu. 2018

73 citations recorded by the source corpus.

Medical data are valuable for improvement of health care, policy making and many other purposes. Vast amount of medical data are stored in different locations, on many different devices and in different data silos. Sharing medical data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning ,which is a method that sends machine learning algorithms simultaneously to all data sources, train models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them.One challenge in applying federated machine learning is the heterogeneity of data from different sources. To tackle this problem, we proposed an adaptive boosting method that increases the efficiency of federated machine learning. Using intensive care unit data from hospital, we showed that LoAdaBoost federated learning outperformed baseline method and increased communication efficiency at negligible additional cost.

Subfields: Artificial Intelligence, Health Informatics, Federated & Distributed Learning, Medical & Health AI

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Citations in the corpus

Federated Optimization in Heterogeneous Networks (2018, background): “…on network bandwidth, sending local data to the server (Jeong et al., 2018) violates the key privacy assumption of federated learning, and sending globally-shared proxy data to all devices (Zhao et al., 2018; Huang et al., 2018) requires effort to carefully generate or collect such auxiliary data.”
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization (2019, background): “Examples of such applications are learning from wearable devices [Huang et al., 2018], learning sentiment [Smith et al., 2017], and location-based services [Samarakoon et al., 2018].”
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records (2019, background): “Moreover, unlike other optimization algorithms for federated learning on non-IID data [26-28] that required a fraction of all data to be shared across the clients, CBFL obviated any degrees of patient data transmission, thereby keeping privacy intact.”
Eavesdrop the Composition Proportion of Training Labels in Federated Learning (2019, background): “…activity recognition [1], heart rate monitoring [4] and keyboard prediction [2, 3]), but also in other ﬁelds, such as healthcare industry (e.g., disease diagnosis online expert sys-tem and medical insurance registration [31]) and transportation systems (vehicular networking technology [6, 7]).”
Two-stage Federated Phenotyping and Patient Representation Learning (2019, methodology): “The strategy of federated learning has been recently adopted in the medical field in structured databased machine learning tasks (Liu et al., 2018a; Huang et al., 2018; Liu et al., 2018b).”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “The study in [144] propose Loss-based Adaptive Boosting (LoAdaBoost) FedAvg to further improve upon FedAvg.”
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning (2021, methodology): “…do Broadcasting x t +1 to workers. end for For non-i.i.d. datasets, many works (Sattler et al., 2019; Zhao et al., 2018; Li et al., 2018; Wang et al., 2019a; Karimireddy et al., 2019; Huang et al., 2018; Jeong et al., 2018) heuristically demonstrated the performance of FedAvg and its variants.”
Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, methodology): “[56] employ a cross-entropy analysis to evaluate the quality of local models before sending them to the aggregation server, which defines the threshold that characterizes a good model.”
Multi-hop Federated Private Data Augmentation with Sample Compression (2019, background): “On the other hand, global proxy data can be either collected or gathered at the edge server [Zhang et al., 2017; Huang et al., 2018], yet it costs an additional effort to acquire such a dataset.”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, methodology): “, through the aforementioned LoAdaBoost FedAvg in [76].”