no code implementations • 5 Feb 2024 • Shanshan Han, Qifan Zhang, Yuhang Yao, Weizhao Jin, Zhaozhuo Xu, Chaoyang He
This paper explores existing works of multi-agent systems and identifies challenges that remain inadequately addressed.
no code implementations • 6 Oct 2023 • Shanshan Han, Wenxuan Wu, Baturalp Buyukates, Weizhao Jin, Qifan Zhang, Yuhang Yao, Salman Avestimehr, Chaoyang He
Federated Learning (FL) systems are vulnerable to adversarial attacks, where malicious clients submit poisoned models to prevent the global model from converging or plant backdoors to induce the global model to misclassify some samples.
no code implementations • 2 Jul 2023 • Sara Babakniya, Zalan Fabian, Chaoyang He, Mahdi Soltanolkotabi, Salman Avestimehr
Deep learning models are prone to forgetting information learned in the past when trained on new data.
1 code implementation • 8 Jun 2023 • Shanshan Han, Baturalp Buyukates, Zijian Hu, Han Jin, Weizhao Jin, Lichao Sun, Xiaoyang Wang, Wenxuan Wu, Chulin Xie, Yuhang Yao, Kai Zhang, Qifan Zhang, Yuhui Zhang, Carlee Joe-Wong, Salman Avestimehr, Chaoyang He
This paper introduces FedSecurity, an end-to-end benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
1 code implementation • 20 Mar 2023 • Weizhao Jin, Yuhang Yao, Shanshan Han, Carlee Joe-Wong, Srivatsan Ravi, Salman Avestimehr, Chaoyang He
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.
1 code implementation • 3 Mar 2023 • Zhenheng Tang, Xiaowen Chu, Ryan Yide Ran, Sunwoo Lee, Shaohuai Shi, Yonggang Zhang, Yuxin Wang, Alex Qiaozhong Liang, Salman Avestimehr, Chaoyang He
It improves the training efficiency, remarkably relaxes the requirements on the hardware, and supports efficient large-scale FL experiments with stateful clients by: (1) sequential training clients on devices; (2) decomposing original aggregation into local and global aggregation on devices and server respectively; (3) scheduling tasks to mitigate straggler problems and enhance computing utility; (4) distributed client state manager to support various FL algorithms.
no code implementations • 27 Feb 2023 • Baturalp Buyukates, Chaoyang He, Shanshan Han, Zhiyong Fang, Yupeng Zhang, Jieyi Long, Ali Farahanchi, Salman Avestimehr
Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e. g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i. e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design.
no code implementations • 2 Feb 2023 • Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han, Shantanu Sharma, Chaoyang He, Sharad Mehrotra, Salman Avestimehr
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e. g., mobile devices) or silo-ed institutional entities (e. g., hospitals, banks) without sharing the data among parties.
no code implementations • 10 Dec 2022 • Chaoyang He, Shuai Zheng, Aston Zhang, George Karypis, Trishul Chilimbi, Mahdi Soltanolkotabi, Salman Avestimehr
The mixture of Expert (MoE) parallelism is a recent advancement that scales up the model size with constant computational cost.
1 code implementation • 10 Oct 2022 • Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
no code implementations • 11 Jan 2022 • Sunwoo Lee, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr
We propose a partial model averaging framework that mitigates the model discrepancy issue in Federated Learning.
no code implementations • 27 Dec 2021 • Erum Mushtaq, Chaoyang He, Jie Ding, Salman Avestimehr
However, given that clients' data are invisible to the server and data distributions are non-identical across clients, a predefined architecture discovered in a centralized setting may not be an optimal solution for all the clients in FL.
no code implementations • 21 Dec 2021 • Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen
Specifically, we design both a micro and a macro search space to model possible architectures of ST-blocks and the connections among heterogeneous ST-blocks, and we provide a search strategy that is able to jointly explore the search spaces to identify optimal forecasting models.
1 code implementation • 22 Nov 2021 • Chaoyang He, Alay Dilipbhai Shah, Zhenheng Tang, Di Fan1Adarshan Naiynar Sivashunmugam, Keerti Bhogaraju, Mita Shimpi, Li Shen, Xiaowen Chu, Mahdi Soltanolkotabi, Salman Avestimehr
To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection.
no code implementations • 15 Nov 2021 • Tuo Zhang, Lei Gao, Chaoyang He, Mi Zhang, Bhaskar Krishnamachari, Salman Avestimehr
In this paper, we will discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications.
1 code implementation • NeurIPS 2021 • Geng Yuan, Xiaolong Ma, Wei Niu, Zhengang Li, Zhenglun Kong, Ning Liu, Yifan Gong, Zheng Zhan, Chaoyang He, Qing Jin, Siyue Wang, Minghai Qin, Bin Ren, Yanzhi Wang, Sijia Liu, Xue Lin
Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works.
no code implementations • 19 Oct 2021 • Sunwoo Lee, Tuo Zhang, Chaoyang He, Salman Avestimehr
In Federated Learning, a common approach for aggregating local models across clients is periodic averaging of the full model parameters.
no code implementations • 6 Oct 2021 • Chaoyang He, Zhengyu Yang, Erum Mushtaq, Sunwoo Lee, Mahdi Soltanolkotabi, Salman Avestimehr
In this paper we propose self-supervised federated learning (SSFL), a unified self-supervised and personalized federated learning framework, and a series of algorithms under this framework which work towards addressing these challenges.
no code implementations • 2 Oct 2021 • Yahya H. Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, Salman Avestimehr
Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment.
no code implementations • 29 Sep 2021 • Jinhyun So, Chaoyang He, Chien-Sheng Yang, Songze Li, Qian Yu, Ramy E. Ali, Basak Guler, Salman Avestimehr
We also demonstrate that, unlike existing schemes, LightSecAgg can be applied to secure aggregation in the asynchronous FL setting.
no code implementations • 29 Sep 2021 • Chaoyang He, Erum Mushtaq, Jie Ding, Salman Avestimehr
Federated Learning (FL) is an effective learning framework used when data cannotbe centralized due to privacy, communication costs, and regulatory restrictions. While there have been many algorithmic advances in FL, significantly less effort hasbeen made on model development, and most works in FL employ predefined modelarchitectures discovered in the centralized environment.
2 code implementations • 14 Jul 2021 • Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
no code implementations • 12 Jul 2021 • Jiacheng Liang, Songze Li, Bochuan Cao, Wensi Jiang, Chaoyang He
Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution.
1 code implementation • 15 Jun 2021 • Tuo Zhang, Chaoyang He, Tianhao Ma, Lei Gao, Mark Ma, Salman Avestimehr
In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices.
1 code implementation • 4 Jun 2021 • Chaoyang He, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, Salman Avestimehr
This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature.
1 code implementation • 9 May 2021 • Han Huang, Li Shen, Chaoyang He, Weisheng Dong, Wei Liu
Specifically, the cell-level search space is designed based on an information distillation mechanism, focusing on the combinations of lightweight operations and aiming to build a more lightweight and accurate SR structure.
1 code implementation • Findings (NAACL) 2022 • Bill Yuchen Lin, Chaoyang He, Zihang Zeng, Hulin Wang, Yufen Huang, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr
Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks.
1 code implementation • 14 Apr 2021 • Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
1 code implementation • 5 Feb 2021 • Chaoyang He, Shen Li, Mahdi Soltanolkotabi, Salman Avestimehr
PipeTransformer automatically adjusts the pipelining and data parallelism by identifying and freezing some layers during the training, and instead allocates resources for training of the remaining active layers.
2 code implementations • NeurIPS 2020 • Chaoyang He, Murali Annavaram, Salman Avestimehr
However, the large model size impedes training on resource-constrained edge devices.
5 code implementations • 27 Jul 2020 • Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr
Federated learning (FL) is a rapidly growing research field in machine learning.
1 code implementation • 18 Apr 2020 • Chaoyang He, Murali Annavaram, Salman Avestimehr
Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions.
1 code implementation • CVPR 2020 • Chaoyang He, Haishan Ye, Li Shen, Tong Zhang
To remedy this, this paper proposes \mldas, a mixed-level reformulation for NAS that can be optimized efficiently and reliably.
8 code implementations • 10 Dec 2019 • Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
1 code implementation • 11 Oct 2019 • Chaoyang He, Conghui Tan, Hanlin Tang, Shuang Qiu, Ji Liu
However, in many social network scenarios, centralized federated learning is not applicable (e. g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable).
no code implementations • 4 Jul 2019 • Zi Long, Lianzhi Tan, Shengping Zhou, Chaoyang He, Xin Liu
Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage.
1 code implementation • 27 Jun 2019 • Chaoyang He, Tian Xie, Yu Rong, Wenbing Huang, Junzhou Huang, Xiang Ren, Cyrus Shahabi
Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.
no code implementations • 27 Jun 2019 • Chaoyang He, Ming Li
The spatial anti-aliasing technique for line joins (intersections of the road segments) on vector maps is exclusively crucial to visual experience and system performance.
Graphics Computational Geometry