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Active Federated Learning [article]

Jack Goetz, Kshitiz Malik, Duc Bui, Seungwhan Moon, Honglei Liu and Anuj Kumar
2019 arXiv   pre-print
To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client  ...  Federated Learning allows for population level models to be trained without centralizing client data by transmitting the global model to clients, calculating gradients locally, then averaging the gradients  ...  (AFL), the first user cohort selection technique for FL which actively adapts to the state of the model and the data on each client.  ... 
arXiv:1909.12641v1 fatcat:rybnm46hs5d7zmprnv2gi3zfui

Asynchronous Federated Learning on Heterogeneous Devices: A Survey [article]

Chenhao Xu, Youyang Qu, Yong Xiang, Longxiang Gao
2022 arXiv   pre-print
With the increased computing and communication capabilities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models becomes a trend.  ...  Federated learning (FL) is experiencing a fast booming with the wave of distributed machine learning.  ...  Besides, based on transfer learning or meta-learning, asynchronous personalized local model training is potentially an effective and accurate solution.  ... 
arXiv:2109.04269v3 fatcat:bcix56mg7zev7hzav4rahkycai

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning [article]

Shaoxiong Ji and Teemu Saravirta and Shirui Pan and Guodong Long and Anwar Walid
2021 arXiv   pre-print
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.  ...  Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods  ...  [31] adapted MAML into the federated framework Per-FedAvg, to learn an initial shared model, leading to fast adaption and personalization for each client.  ... 
arXiv:2102.12920v2 fatcat:5fcwfhxibbedbcbuzrfyqdedky

Loss Tolerant Federated Learning [article]

Pengyuan Zhou, Pei Fang, Pan Hui
2021 arXiv   pre-print
Recent solutions have been focusing on threshold-based client selection schemes to guarantee the communication efficiency.  ...  In this paper, we explore the loss tolerant federated learning (LT-FL) in terms of aggregation, fairness, and personalization.  ...  Integrating TRA with state-of-the-art algorithms shows outperforming performances on aggregation, fairness, and personalization in most scenarios.  ... 
arXiv:2105.03591v1 fatcat:vpfrw3f6yfapthyk2mxypze2ca

Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems

Shaashwat Agrawal, Aditi Chowdhuri, Sagnik Sarkar, Ramani Selvanambi, Thippa Reddy Gadekallu, Qiangqiang Yuan
2021 Computational Intelligence and Neuroscience  
In this paper, a novel temporal model averaging algorithm is proposed for asynchronous federated learning (AFL).  ...  Federated learning (FL) is an emerging subdomain of machine learning (ML) in a distributed and heterogeneous setup.  ...  on the observed training times, model complexity, and average communication cost.  ... 
doi:10.1155/2021/5844728 pmid:34956350 pmcid:PMC8709749 fatcat:zgvapqnjinfqvakuxg3jak5x6e

Mitigating Data Heterogeneity in Federated Learning with Data Augmentation [article]

Artur Back de Luca, Guojun Zhang, Xi Chen, Yaoliang Yu
2022 arXiv   pre-print
Federated Learning (FL) is a prominent framework that enables training a centralized model while securing user privacy by fusing local, decentralized models.  ...  , and obtain higher accuracy on unseen clients.  ...  Federated Learning; Gen-AFL: Generalized AFL; VM: Variance minimization.  ... 
arXiv:2206.09979v1 fatcat:cdyxia2hlzd3nad63g2sshgm6y

AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation [article]

Qiyuan Wang, Qianqian Yang, Shibo He, Zhiguo Shi, Jiming Chen
2022 arXiv   pre-print
In this paper, we present an asynchronous federated learning framework with a proposed adaptive weight aggregation algorithm, referred to as AsyncFedED.  ...  However, it introduces the stale model problem, where the newly arrived update was calculated based on a set of stale weights that are older than the current global model, which may hurt the convergence  ...  Euclidean Distance based Adaptive Federated Aggregation In this section, we introduce the proposed AsyncFedED with an Euclidean distance based adaptive federated aggregation scheme that adapts the global  ... 
arXiv:2205.13797v2 fatcat:bmtagswx7jelro37vgqqj3u4mm

Enhancing Canadian Teacher Education Using a Story Framework

Susan Drake
2010 Canadian Journal for the Scholarship of Teaching and Learning  
Literacy and emerging new literacies are explored within the framework, as well as traditional assessment and assessment for and as learning.  ...  This framework uses an inside-outside/past-future approach to analyze current issues and includes personal, cultural and global perspectives.  ...  According to the model, people essentially make meaning from stories -both personal and social ones -and recognize that we have the power to change the stories if we wish.  ... 
doi:10.5206/cjsotl-rcacea.2010.2.2 fatcat:hkagqxmcd5cvzbvloio73mf2t4

Agnostic Federated Learning [article]

Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
2019 arXiv   pre-print
A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.  ...  Beyond federated learning, our framework and algorithm can be of interest to other learning scenarios such as cloud computing, domain adaptation, drifting, and other contexts where the training and test  ...  Domain features and personalization We studied agnostic federated learning, where we learn a model that performs well on all domains.  ... 
arXiv:1902.00146v1 fatcat:5uv3gbio65boddwuanm2ryh57q

GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization [article]

Yen-Hsiu Chou, Shenda Hong, Chenxi Sun, Derun Cai, Moxian Song, Hongyan Li
2021 arXiv   pre-print
With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue.  ...  We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue by considering a single global model and multiple local models for each client.  ...  In summary, our contributions are three-fold: • We present GRP-FED to simultaneously consider global fairness and local personalization for Federated Learning; • The proposed adaptively-aggregated global  ... 
arXiv:2108.13858v1 fatcat:fwc47tl7breifi2lhb5lqsixgi

Fair Resource Allocation in Federated Learning [article]

Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
2020 arXiv   pre-print
We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms  ...  Federated learning involves training statistical models in massive, heterogeneous networks.  ...  ACKNOWLEDGMENTS We thank Sebastian Caldas, Chen Dan, Neel Guha, Anit Kumar Sahu, Eric Tan, and Samuel Yeom for their helpful discussions and comments.  ... 
arXiv:1905.10497v2 fatcat:z575xwuktnhotpawxu7rvoyxsu

Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management [article]

Helin Yang, Jun Zhao, Zehui Xiong, Kwok-Yan Lam, Sumei Sun, Liang Xiao
2020 arXiv   pre-print
Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, artificial intelligence (AI) model training, and wireless communications.  ...  In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without  ...  Fig. 1 . 1 Federated learning-based UAV-enabled wireless networks.  ... 
arXiv:2011.14197v1 fatcat:jrwqdrdfkbh3joqa2nichlclxu

Federated Learning for Healthcare Informatics [article]

Jie Xu and Benjamin S. Glicksberg and Chang Su and Peter Walker and Jiang Bian and Fei Wang
2020 arXiv   pre-print
Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect  ...  In particular, we summarize the general solutions to the statistical challenges, system challenges and privacy issues in federated learning, and point out the implications and potentials in healthcare.  ...  Acknowledgements The work is supported by ONR N00014-18-1-2585 and NSF 1750326. Conflict of interest The authors declare that they have no conflict of interest.  ... 
arXiv:1911.06270v2 fatcat:kvsrmvup4rhkxb2lp37tgoc22i


E. Caroline Wylie, Laura Goe, Dawn Marie Leusner, Christine J. Lyon, Cynthia Tocic, E. Caroline Wylie, Donna Cleland, Maureen Gannon, Judith Ellsworth, Margaret Heritage, Jeff Maher, Diane Mardy (+5 others)
2008 ETS Research Report Series  
All of the current ETS staff, along with Dylan Wiliam and Marnie Thompson, worked at ETS for several years on an iterative research and development program, out of which grew the Keeping Learning on Track  ...  As part of its educational and social mission and in fulfilling the organization's nonprofit charter and bylaws, ETS has and continues to learn from and also to lead research that furthers educational  ...  , via sustained, school-based teacher learning communities (TLCs).  ... 
doi:10.1002/j.2333-8504.2008.tb02115.x fatcat:vkkxkjbj6naopayqcymyvrokxm

Leadership Development as a Driver of Equity and Inclusion

Sally M. Alvarez, Jose F. Alvarez
2018 Work and occupations  
, using a variety of learning modes; providing a safe space and what Kurt Lewin describes as "a community of practice" where difficult challenges can be tackled collectively; and using leadership development  ...  Those factors venturing beyond the traditional "skill-building " approach of most labor leadership training toward a more transformational model of leadership development; an emphasis on experiential learning  ...  a new theory anda new model based on it in 2003.  ... 
doi:10.1177/0730888418786337 fatcat:tij5kio63fa4nbcgqj6tjx77hu
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