A Selective Model Aggregation Approach in Federated Learning for Online Anomaly Detection

Yang, Qin; Matsutani, Hiroki; Kondo, Masaaki

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00119

Cited by 13 publications

(8 citation statements)

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“…In Qin et al (2020), the authors worked on online anomaly detection in FL. They focus on ML methods to build local anomaly detection on edge devices.…”

Section: Fl Applicationsmentioning

confidence: 99%

Towards federated learning: An overview of methods and applications

Silva

Vinagre

Gama

2023

WIREs Data Min & Knowl

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Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an indepth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.

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“…In Qin et al (2020), the authors worked on online anomaly detection in FL. They focus on ML methods to build local anomaly detection on edge devices.…”

Section: Fl Applicationsmentioning

confidence: 99%

Towards federated learning: An overview of methods and applications

Silva

Vinagre

Gama

2023

WIREs Data Min & Knowl

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“…The more machine/institutions are taking part in a federation, the more important is the ability to scale. As mentioned in the previous Section, to the best of our knowledge, a consolidated way for detecting "poor" training contributions (coming from institutions with corrupted or redundant data) is still missing, and aggregation functions are currently being evaluated by the research community [26] [31] [33]. Another implication when talking about big scales is represented by the infrastructure and the connectivity chosen by the institutions for communication [14].…”

Section: Fl Challengesmentioning

confidence: 99%

FeLebrities: a user-centric assessment of Federated Learning frameworks

Riviera¹,

Menegaz²,

Galazzo³

2023

Preprint

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<p>Federated Learning (FL) is a new paradigm that aims at solving the data access problem. It is gaining an increasing interest in a variety of research fields, including the Biomedical and Financial environments, where lots of valuable data sources are available but not often directly accessible due to the regulations that protect sensitive information. FL provides a wayout enabling the processing and sharing of data modeling solutions moving the focus from data to models. The FL paradigm involves different entities (institutions) holding proprietary datasets, contributing with each other to locally train a copy of a shared Artificial Intelligence (AI) model. Although there are different studies in the literature that suggest how to conceptually implement and orchestrate a federation, fewer efforts have been made on practical implications. With the ambition of helping accelerating the exploitation of FL frameworks, this paper proposes a survey of public tools that are currently available, an objective ranking based on current state of user preferences and the assessment of the growth trend of the tool popularity over a six months time window. Finally, a ranking of the tools maturity is derived based on key aspects to consider when building a FL pipeline.</p>

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“…Rather than simple adoption, other papers seek to improve the FL framework in various ways. Study [29] concerns that some clients participating in FL could have bad data or be under attack, and thus will send low-quality weights to the aggregation server that negatively affects the global model. It proposes the server to test each local model sent by each client on some preset data first, and discarding those that yield a too high loss value.…”

Section: Federated Learningmentioning

confidence: 99%