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Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques
[article]
2021
arXiv
pre-print
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We study the optimization aspects of personalized Federated Learning (FL). ...
By studying a general personalized objective that is capable of recovering essentially any existing personalized FL objective as a special case, we develop a universal optimization theory applicable to ...
Therefore, our results often deem the optimization tailored to solve a specific personalized FL unnecessary. Universal (convex) optimization methods and theory for personalized FL. ...
arXiv:2102.09743v3
fatcat:jnnxck463jabvcflm2cb3wc3xi
Personalized Federated Learning with Gaussian Processes
[article]
2021
arXiv
pre-print
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Personalized federated learning (PFL) further extends this setup to handle data heterogeneity between clients by learning personalized models. ...
Federated learning aims to learn a global model that performs well on client devices with limited cross-client communication. ...
Acknowledgements This study was funded by a grant to GC from the Israel Science Foundation (ISF 737/2018), and by an equipment grant to GC and Bar-Ilan University from the Israel Science Foundation (ISF ...
arXiv:2106.15482v2
fatcat:ajmf5yb3yncx3h77jzqyjggxfi
Personalized Federated Learning with Multiple Known Clusters
[article]
2022
arXiv
pre-print
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We consider the problem of personalized federated learning when there are known cluster structures within users. ...
We study a hierarchical linear model to theoretically demonstrate that our approach outperforms agents learning independently and agents learning a single shared weight. ...
Fishman Faculty Research Fund at the University of Chicago Booth School of Business. This work was completed in part with resources provided by the University of Chicago Research Computing Center. ...
arXiv:2204.13619v1
fatcat:abh7ccqttjdqjehrxbgfsyhpfq
An Optimal Transport Approach to Personalized Federated Learning
[article]
2022
arXiv
pre-print
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In this paper, we focus on this problem and propose a novel personalized Federated Learning scheme based on Optimal Transport (FedOT) as a learning algorithm that learns the optimal transport maps for ...
We then leverage the results on multi-marginal optimal transport problems to formulate FedOT as a min-max optimization problem and analyze its generalization and optimization properties. ...
To learn the personalized models under the above condition, we introduce FedOT as a Federated learning framework based on Optimal Transport. ...
arXiv:2206.02468v1
fatcat:caibjnxanjccxkbkgclriins4e
Free Movement of Persons and European Solidarity Revisited
2015
Perspectives on Federalism
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The most recent case-law shows, in fact, a spectacular retreat from this rhetoric in tune with the neo-nationalistic and social-chauvinistic moods prevailing in Europe. ...
This paper analyses the case-law of the European Court of Justice on the scope and limits of cross-border access of economically inactive Union citizens to national systems of social assistance. ...
XI In essence, this is the lesson learned from Brey and Dano. ...
doi:10.1515/pof-2015-0016
fatcat:ox6nf6mrkjbe3a42nwq2lo4gbm
Knowledge and Perception of Undergraduate Students towards Nutrigenomics for Personalized Nutrition in Federal University of Agriculture, Abeokuta, Ogun State
2022
Zenodo
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Participants in this study were students from the Federal University of Agriculture, Abeokuta Ogun state who completed a paper survey questionnaire administered. ...
Nutrigenomics is a scientific study of the molecular interaction between genes and nutrients. ...
Federal University of Agriculture, Abeokuta. ...
doi:10.5281/zenodo.6612655
fatcat:5mzndb75hnd5bpi4eagm3eob3a
Cross-Silo Federated Learning: Challenges and Opportunities
[article]
2022
arXiv
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Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private. ...
Based on the participating clients and the model training scale, federated learning can be classified into two types: cross-device FL where clients are typically mobile devices and the client number can ...
Earlier work in [14] established a unified framework on fair resource allocation, and it is important to borrow their techniques on the unified definition of fairness and derive potential solutions in ...
arXiv:2206.12949v1
fatcat:emc4wbfdfrey5p2zivc66yugsm
Autonomous Learning System Towards Mobile Intelligence
2021
International Journal of Software and Informatics
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Furthermore, by optimization techniques such as model compression, neural network compiler, and runtime cache reuse, AutLearn can significantly reduce the on-client training cost. ...
How to efficiently deploy machine learning models on mobile devices has drawn a lot of attention in both academia and industries, among which the model training is a critical part. ...
Therefore, federated learning protects user privacy to a certain extent. A lot of follow-up work focuses on how to optimize the process of federated learning. ...
doi:10.21655/ijsi.1673-7288.00247
fatcat:fxbhsznxdvcs3aflfqwpmdlgou
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy
[article]
2022
arXiv
pre-print
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We investigate the existing security challenges in federated learning and provide a comprehensive overview of established defense techniques for data poisoning, inference attacks, and model poisoning attacks ...
This article provides a systematic overview and detailed taxonomy of federated learning. ...
Federated Transfer learning utilizes the classic machine learning-based transfer learning (Pan and Yang 2009) technique to train a new requirement on a pre-trained framework that has been already trained ...
arXiv:2204.13697v1
fatcat:rvlsrnk66jblzguy2vnh3thgtu
Experiential learning at Lean-Thinking-Learning Space
2019
International Journal on Interactive Design and Manufacturing
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Therefore, a background explanation of the proposed learning model and a learning space called Lean-Thinking-Learning Space are elaborated. ...
This work presents a literature review on competency-based education, experiential learning and challenge-based learning. ...
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s12008-019-00578-3
fatcat:y4wn26ipgzcilcbsrqkirll3ve
BILDU: Compile, Unify, Wrap, and Share Digital Learning Resources
2008
Interdisciplinary Journal of e-Skills and Lifelong Learning
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This paper gives an overview of current search technologies and proposes to develop a social search framework that would adapt generic search paradigms to the specific characteristics of learning resources ...
The idea of the framework is based on the following concepts: compile (indexation of learning resource repositories and creation of vertical search engines), unify (integration of vertical search engines ...
This set of ideas will assist the design of BILDU (a word in Basque which means compile, unify, wrap, and gather), a search framework prototype that is intended to validate these concepts. ...
doi:10.28945/369
fatcat:n3ud5jiwdbfjxmpc2mfgmgvztq
Federated Learning: A Signal Processing Perspective
[article]
2021
arXiv
pre-print
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In this article, we provide a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools ...
We present a formulation for the federated learning paradigm from a signal processing perspective, and survey a set of candidate approaches for tackling its unique challenges. ...
Here, one can consider choosing a subset of the data used for training via active learning techniques, which dates back to Chernoff's framework of optimal experimental design [59] , in order to reduce ...
arXiv:2103.17150v2
fatcat:pktgiqowsjbklfnj753ehdbnhu
Federated Learning for Smart Healthcare: A Survey
[article]
2021
arXiv
pre-print
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The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. ...
Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training ...
Another joint optimization of device scheduling and resource allocation framework is investigated in [42] . ...
arXiv:2111.08834v1
fatcat:jmex4e25rbgy3bk67iolrj4uee
A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology
2022
Information
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Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. ...
The study also examined applicable machine learning models for federated learning. ...
In a federated setup, Pfohl et al. [44] investigated differentially secluded learning for EMR. They also showed that the results are equivalent to training in a unified environment. Huang et al. ...
doi:10.3390/info13050263
fatcat:evmwjo52jfhlbg4vw2hatew4k4
Federated learning and next generation wireless communications: A survey on bidirectional relationship
[article]
2022
arXiv
pre-print
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Towards this end, a distributed machine learning paradigm termed as Federated learning (FL) has been proposed recently. ...
Thus, a 'bidirectional' relationship exists between FL and wireless communications. ...
Federated personalization Local datasets on client devices have different varying features correlated with the personal preferences and characteristics of the user. ...
arXiv:2110.07649v2
fatcat:4grniockzjbbrcl6uqfhsrhhfy
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