FedDL

Tu, Linlin; Ouyang, Xiaomin; Zhou, Jiayu; He, Yuze; Xing, Guoliang

doi:10.1145/3485730.3485946

Cited by 59 publications

(22 citation statements)

References 42 publications

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“…Deep Neural Networks (DNNs) have shown remarkable performance in various applications such as computer vision [43,75], natural language understanding [56,76], human activity recognition [53,62], and etc. Despite their strong capabilities, it usually requires a huge volume of training data, which is usually produced and stored on edge devices (e.g., mobile phones).…”

Section: Introductionmentioning

confidence: 99%

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Zhuang

Huang

et al. 2019

Mathematical Biosciences and Engineering

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The crystallization kinetics and melting behavior of nylon 10,10 in neat nylon 10,10 and in nylon 10,10 -montmorillonite (MMT) nanocomposites were systematically investigated by differential scanning calorimetry. The crystallization kinetics results show that the addition of MMT facilitated the crystallization of nylon 10,10 as a heterophase nucleating agent; however, when the content of MMT was high, the physical hindrance of MMT layers to the motion of nylon 10,10 chains retarded the crystallization of nylon 10,10, which was also confirmed by polarized optical microscopy. However, both nylon 10,10 and nylon 10,10 -MMT nanocomposites exhibited multiple melting be-havior under isothermal and nonisothermal crystallization conditions. The temperature of the lower melting peak (peak I) was independent of MMT content and almost remained constant; however, the temperature of the highest melting peak (peak II) decreased with increasing MMT content due to the physical hindrance of MMT layers to the motion of nylon 10,10 chains.

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Section: Introductionmentioning

confidence: 99%

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Zhuang

Huang

et al. 2019

Mathematical Biosciences and Engineering

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“…Wu et al [ 36 ] proposed a generative convolutional autoencoder network and fine-tuned the model parameters of higher layers of generative convolutional autoencoder to obtain more accurate personalized models. Another way to address statistical heterogeneity is user clustering, which enables the FL system to capture the underlying relationships between users as studied by Ouyang et al [ 34 ] and Tu et al [ 35 ]. By clustering users, those in the same group can collaboratively learn personalized models.…”

Section: Resultsmentioning

confidence: 99%

“…Ouyang et al [ 34 ] designed a learned cluster structure for their system, which allows the use of clusterwise straggler dropout and correlation-based node selection to reduce communication overhead. Moreover, Tu et al [ 35 ] proposed FedDL, which can reduce the number of parameters communicated between users and the server through a dynamic layerwise sharing scheme. This is because only the lower layers of local models need to be uploaded to the server for global training.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, Ouyang et al [ 34 ] leveraged the inherent relationships between users to dynamically drop nodes during the FL process, where the server will drop users who converge slower than others within the same cluster or are less related to others in the same cluster. Tu et al [ 35 ] used the similarity among users’ model weights to learn the layerwise sharing structure, which can be regarded as an asynchronous scheme that mitigates the impact of straggling users in heterogeneous environments. Furthermore, as proposed by Zhang et al [ 16 ], the bottom-up design of the new local update scheme and the adaptive global update scheme allows the FL system to meet device-specific optimization goals (eg, energy savings) while strictly protecting user privacy.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Federated Learning in Mobile Health: Scoping Review

Wang¹,

Du²,

Gong³

et al. 2023

J Med Internet Res

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Background The proliferation of mobile health (mHealth) applications is partly driven by the advancements in sensing and communication technologies, as well as the integration of artificial intelligence techniques. Data collected from mHealth applications, for example, on sensor devices carried by patients, can be mined and analyzed using artificial intelligence–based solutions to facilitate remote and (near) real-time decision-making in health care settings. However, such data often sit in data silos, and patients are often concerned about the privacy implications of sharing their raw data. Federated learning (FL) is a potential solution, as it allows multiple data owners to collaboratively train a machine learning model without requiring access to each other’s raw data. Objective The goal of this scoping review is to gain an understanding of FL and its potential in dealing with sensitive and heterogeneous data in mHealth applications. Through this review, various stakeholders, such as health care providers, practitioners, and policy makers, can gain insight into the limitations and challenges associated with using FL in mHealth and make informed decisions when considering implementing FL-based solutions. Methods We conducted a scoping review following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched 7 commonly used databases. The included studies were analyzed and summarized to identify the possible real-world applications and associated challenges of using FL in mHealth settings. Results A total of 1095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were included in the review. The analysis of these articles revealed 2 main application areas for FL in mHealth, that is, remote monitoring and diagnostic and treatment support. More specifically, FL was found to be commonly used for monitoring self-care ability, health status, and disease progression, as well as in diagnosis and treatment support of diseases. The review also identified several challenges (eg, expensive communication, statistical heterogeneity, and system heterogeneity) and potential solutions (eg, compression schemes, model personalization, and active sampling). Conclusions This scoping review has highlighted the potential of FL as a privacy-preserving approach in mHealth applications and identified the technical limitations associated with its use. The challenges and opportunities outlined in this review can inform the research agenda for future studies in this field, to overcome these limitations and further advance the use of FL in mHealth.

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“…The main drawback of these approaches is that they consider a convex objective function, which is not suitable for complex HAR models based on deep learning. Recently, a few works proposed solutions based on Federated Clustering [37,38,13]. The goal of Federated Clustering is to create specialized global models (server-side) by grouping users that perform activities in a similar way.…”

Section: Related Workmentioning

confidence: 99%