Fetching the paper…
Reading the bibliography…
Federated learning (FL) has gained substantial attention in recent years due to the data privacy concerns related to the pervasiveness of consumer devices that continuously collect data from users.
Events detection for an audio-based surveillance system
Chloé Clavel, Thibaut Ehrette, and Gaël Richard · 2005
Earlier work this paper cites.
Iemocap: interactive emotional dyadic motion capture database
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Ebrahim (Abe) Kazemzadeh, Emily Mower Provost, Samuel Kim, Jeannette N. Chang, Sungbok Lee, and Shrikanth S. Narayanan · 2008
Earlier work this paper cites.
Crema-d: Crowd-sourced emotional multimodal actors dataset
Houwei Cao, David G Cooper, Michael K Keutmann, Ruben C Gur, Ani Nenkova, and Ragini Verma · 2014
Earlier work this paper cites.
A dataset and taxonomy for urban sound research
J. Salamon, C. Jacoby, and J. P. Bello · 2014
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Z. Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zhang · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
Earlier work this paper cites.
Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. B. McMahan, Virginia Smith, and Ameet S. Talwalkar · 2018
Earlier work this paper cites.
Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
Earlier work this paper cites.
Attention based fully convolutional network for speech emotion recognition
Yuanyuan Zhang, Jun Du, Zirui Wang, and Jian shu Zhang · 2018
Earlier work this paper cites.
An unsupervised autoregressive model for speech representation learning
Yu-An Chung, Wei-Ning Hsu, Hao Tang, and James R. Glass · 2019
Cited alongside, same era.
A federated approach in training acoustic models
Dimitrios Dimitriadis, Ken’ichi Kumatani, Robert Gmyr, Yashesh Gaur, and Sefik Emre Eskimez · 2020
Cited alongside, same era.
Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr · 2020
Cited alongside, same era.
Federated acoustic modeling for automatic speech recognition
Xiaodong Cui, Songtao Lu, and Brian Kingsbury · 2021
Cited alongside, same era.
Training speech recognition models with federated learning: A quality/cost framework
Dhruv Guliani, Françoise Beaufays, and Giovanni Motta · 2021
Cited alongside, same era.
Adaptive federated optimization
Sashank J. Reddi, Zachary B. Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, and H. B. McMahan · 2021
Later among the works it cites.
Federated learning for internet of things
Tuo Zhang, Chaoyang He, Tian-Shya Ma, Mark Ma, and Salman Avestimehr · 2021
Later among the works it cites.
Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings
Jean Ogier du Terrail, Samy Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Tele’nczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, and Mathieu Andreux · 2022
Closest in time.
Federated self-supervised learning for acoustic event classification
Meng Feng, Chieh-Chi Kao, Qingming Tang, Ming Sun, Viktor Rozgic, Spyros Matsoukas, and Chao Wang · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fedgraphnn: A federated learning system and benchmark for graph neural networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr · 2021
Cited alongside, same era.
Fedcv: A federated learning framework for diverse computer vision tasks
Chaoyang He, Alay Shah, Zhenheng Tang, Dian Fan, Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, and Salman Avestimehr · 2021
Cited alongside, same era.
Fednlp: A research platform for federated learning in natural language processing
Bill Yuchen Lin, Chaoyang He, ZiHang Zeng, Hulin Wang, Yufen Huang, Mahdi Soltanolkotabi, Xiang Ren, and Salman Avestimehr · 2021
Cited alongside, same era.
Confident learning: Estimating uncertainty in dataset labels
Curtis G. Northcutt, Lu Jiang, and Isaac L. Chuang · 2021
Cited alongside, same era.
Tiantian Feng and Shrikanth S. Narayanan · 2022
Closest in time.
Federated self-supervised speech representations: Are we there yet?
Yan Gao, Javier Fernández-Marqués, Titouan Parcollet, Abhinav Mehrotra, and Nicholas D. Lane · 2022
Closest in time.
End-to-end speech recognition from federated acoustic models
Yan Gao, Titouan Parcollet, Javier Fernández-Marqués, Pedro Porto Buarque de Gusmão, Daniel J. Beutel, and Nicholas D. Lane · 2022
Closest in time.
Federated learning for the internet of things: Applications, challenges, and opportunities
Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, and Salman Avestimehr · 2022
Closest in time.