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Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques [article]

Filip Hanzely, Boxin Zhao, Mladen Kolar
2021 arXiv   pre-print
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]

Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya
2021 arXiv   pre-print
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]

Boxiang Lyu, Filip Hanzely, Mladen Kolar
2022 arXiv   pre-print
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]

Farzan Farnia, Amirhossein Reisizadeh, Ramtin Pedarsani, Ali Jadbabaie
2022 arXiv   pre-print
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

Stefano Giubboni
2015 Perspectives on Federalism  
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  
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]

Chao Huang, Jianwei Huang, Xin Liu
2022 arXiv   pre-print
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

Mengwei Xu, Institute of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; Key Laboratory of High Confidence Software Technologies of Ministry of Education, Peking University, Beijing 100871, China, Yuanqiang Liu, Kang Huang, Xuanzhe Liu, Gang Huang
2021 International Journal of Software and Informatics  
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]

Sherin Mary Mathews, Samuel A. Assefa
2022 arXiv   pre-print
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

Claudia Lizette Garay-Rondero, Ericka Zulema Rodríguez Calvo, David Ernesto Salinas-Navarro
2019 International Journal on Interactive Design and Manufacturing  
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

Oskar Casquero, Javier Portillo, Manuel Benito, Jesús Romo
2008 Interdisciplinary Journal of e-Skills and Lifelong Learning  
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]

Tomer Gafni, Nir Shlezinger, Kobi Cohen, Yonina C. Eldar, H. Vincent Poor
2021 arXiv   pre-print
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]

Dinh C. Nguyen, Quoc-Viet Pham, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia A. Dobre, Won-Joo Hwang
2021 arXiv   pre-print
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

Roseline Oluwaseun Ogundokun, Sanjay Misra, Rytis Maskeliunas, Robertas Damasevicius
2022 Information  
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]

Debaditya Shome, Omer Waqar, Wali Ullah Khan
2022 arXiv   pre-print
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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