Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework

Wu, Qiong; He, Kaiwen; Chen, Xu

doi:10.48550/arxiv.2002.10671

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…(1) Data heterogeneity and model heterogeneity: In federated learning scenarios, the data of the participants are usually heterogeneous, that is, they may come from different data sources, with different data structures, data types and data distributions. Wu et al [58] summarized the challenges faced by federated learning into three aspects. First, there is heterogeneity in the storage, computing and communication capabilities of various participants.…”

Section: Situation Prediction Robustness Issuesmentioning

confidence: 99%

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Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Wu,

Jin,

2024

Mathematics

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Federated learning is a distributed learning method used to solve data silos and privacy protection in machine learning, aiming to train global models together via multiple clients without sharing data. However, federated learning itself introduces certain security threats, which pose significant challenges in its practical applications. This article focuses on the common security risks of data poisoning during the training phase of federated learning clients. First, the definition of federated learning, attack types, data poisoning methods, privacy protection technology and data security situational awareness are summarized. Secondly, the system architecture fragility, communication efficiency shortcomings, computing resource consumption and situation prediction robustness of federated learning are analyzed, and related issues that affect the detection of data poisoning attacks are pointed out. Thirdly, a review is provided from the aspects of building a trusted federation, optimizing communication efficiency, improving computing power technology and personalized the federation. Finally, the research hotspots of the federated learning data poisoning attack situation prediction are prospected.

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Section: Situation Prediction Robustness Issuesmentioning

confidence: 99%

“…In terms of the comprehensive optimization, Wu et al [58] proposed a collaborative cloud edge framework, PerFit, for personalized federated learning. Intelligent IoT applications benefit from a personalized federated learning framework that resolves equipment issues.…”

Section: Improve the Robustness Of Situational Awarenessmentioning

confidence: 99%

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Wu,

Jin,

2024

Mathematics

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“…Wu et al [9] list three challenges faced by federated learning systems related to personalization: (1) device heterogeneity in terms of storage, computation, and communication capabilities; (2) data heterogeneity arising due to non-IID distribution of data; (3) model heterogeneity arising from situations where different clients need models specifically customized to their environment. As an example of model heterogeneity, consider the sentence: "I live in .....".…”

Section: Need For Personalizationmentioning

confidence: 99%

Survey of Personalization Techniques for Federated Learning

Kulkarni¹,

Kulkarni²,

Pant³

2020

Preprint

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Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.

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“…where f i is the loss of the client i that only depends on his/her own local data. However, there is major criticism of the objective (1) for many of the FL applications [53,25,10]. Specifically, the minimizer of the overall population loss might not be the ideal model for a given client, given that his/her data distribution differs from the population significantly.…”

Section: Introductionmentioning

confidence: 99%

Lower Bounds and Optimal Algorithms for Personalized Federated Learning

Hanzely,

Horváth

et al. 2020

Preprint

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In this work, we consider the optimization formulation of personalized federated learning recently introduced by [19] which was shown to give an alternative explanation to the workings of local SGD methods. Our first contribution is establishing the first lower bounds for this formulation, for both the communication complexity and the local oracle complexity. Our second contribution is the design of several optimal methods matching these lower bounds in almost all regimes. These are the first provably optimal methods for personalized federated learning. Our optimal methods include an accelerated variant of FedProx, and an accelerated variance-reduced version of FedAvg/Local SGD. We demonstrate the practical superiority of our methods through extensive numerical experiments.

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Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework

Cited by 3 publications

References 23 publications

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction

Survey of Personalization Techniques for Federated Learning

Lower Bounds and Optimal Algorithms for Personalized Federated Learning

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