Jupiter: A Modern Federated Learning Platform for Regional Medical Care

Xing, Ju; Jiang, Ze Xun; Yin, Hao

doi:10.1109/jcc49151.2020.00012

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…Jupiter Another such implementation of a federated learning-based IoT infrastructure in the field of medical healthcare on a regional level [42] is: 1) Jupiter: Federated Learning has been continuously proving itself as an efficient mechanism in many fields. Jupiter [13], has the capability to support data tuning, along with providing a Federated Learning facility. Besides, the infrastructure also exposes rich APIs for operations like augmentations, eliminations, and filters, etc.…”

Section: Mini Batch-learningmentioning

confidence: 99%

“…The model, which is in a continuous development phase is managed as sessions, which are identified by a token. For the purpose of secure aggregation, the model is based on Intel SGX [13]. Intel SGX is a set of instructions that increases the security of application code and data, giving them more protection from disclosure or modification.…”

Section: Mini Batch-learningmentioning

confidence: 99%

“…'Jupiter', an advanced federated learning-based infrastructure with API support along with augmentations, filters, and sessions has been discussed in [13]. The platform is one of many implementations that result in well well-trained final model without any compromise in privacy.…”

Section: Introductionmentioning

confidence: 99%

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Survey on Technical Advancements and Renovations in Federated Learning

(Corresponding Author),

Tulshain,

et al. 2024

MJCS

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With the rapid increase in IoT devices and advanced machine learning and deep learning techniques, there has been a growing concern about computational cost and data privacy issues since the data coming from IoT devices is non-independent identically distributed (non-IID). However, the implementation of the federated learning algorithm has proven to be a booster in the performance and a solution to the existing data privacy concerns. This paper gives insight into topics such as Blockchain, Unmanned Aerial Vehicles (UAV), Wireless communication, Vehicular Internet of Things, Healthcare, and Cloud Computing and how they have been implemented and co-related to federated Learning and the application and the emerging use cases in the field of federated learning (FL) with respect to the above-mentioned topics have also been discussed. This paper uniquely shows how federated learning has an edge over the traditional machine learning and deep learning techniques in IoT infrastructure since computing nodes are trained using local models on the devices and then these local models are uploaded to the central global server instead of data directly into a global model on a central server ensuring data privacy.

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Section: Mini Batch-learningmentioning

confidence: 99%

Section: Mini Batch-learningmentioning

confidence: 99%

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Survey on Technical Advancements and Renovations in Federated Learning

(Corresponding Author),

Tulshain,

et al. 2024

MJCS

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“…The authors use DP to enhance the privacy of data shared among IoT devices. On the other hand, Xing et al (2020) proposed Jupiter, an easy-to-use and secure FL platform for regional medical care. It provides a high-performance infrastructure for secure parameter aggregations with dedicated links between aggregators and hospitals.…”

Section: Healthcarementioning

confidence: 99%

Federated learning for 6G-enabled secure communication systems: a comprehensive survey

et al. 2023

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Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in reallife scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches' future directions and existing drawbacks are discussed in detail.

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“…This workflow is also called the single-global-model paradigm. This paradigm has been suggested to be deployed in several fields, including IoT applications [2][3][4], industry applications [5], network applications [6] and so on. While FL offers significant advantages in distributed environments, it concurrently faces substantial security challenges [7,8], particularly from malicious clients.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Robustness within the Collaborative Federated Learning Framework: A Novel Grouping Algorithm for Edge Clients

Su,

Liu,

et al. 2024

Applied Sciences

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In this study, we introduce a novel collaborative federated learning (FL) framework, aiming at enhancing robustness in distributed learning environments, particularly pertinent to IoT and industrial automation scenarios. At the core of our contribution is the development of an innovative grouping algorithm for edge clients. This algorithm employs a distinctive ID distribution function, enabling efficient and secure grouping of both normal and potentially malicious clients. Our proposed grouping scheme accurately determines the numerical difference between normal and malicious groups under various network scenarios. Our method addresses the challenge of model poisoning attacks, ensuring the accuracy of outcomes in a collaborative federated learning framework. Our numerical experiments demonstrate that our grouping scheme effectively limits the number of malicious groups. Additionally, our collaborative FL framework has shown resilience against various levels of poisoning attack abilities and maintained high prediction accuracy across a range of scenarios, showcasing its robustness against poisoning attacks.

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Jupiter: A Modern Federated Learning Platform for Regional Medical Care

Cited by 5 publications

References 2 publications

Survey on Technical Advancements and Renovations in Federated Learning

Survey on Technical Advancements and Renovations in Federated Learning

Federated learning for 6G-enabled secure communication systems: a comprehensive survey

Enhancing Robustness within the Collaborative Federated Learning Framework: A Novel Grouping Algorithm for Edge Clients

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