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.
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.
2 code implementations • 13 Nov 2022 • Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen, Carlee Joe-Wong, Tianqiang Liu
Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door.
no code implementations • 24 May 2022 • Zifan Wang, Yuhang Yao, Chaoran Zhang, Han Zhang, Youjie Kang, Carlee Joe-Wong, Matt Fredrikson, Anupam Datta
Second, our analytical and empirical results demonstrate that feature attribution methods cannot capture the nonlinear effect of edge features, while existing subgraph explanation methods are not faithful.
1 code implementation • NeurIPS 2023 • Yuhang Yao, Weizhao Jin, Srivatsan Ravi, Carlee Joe-Wong
Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated.
no code implementations • 11 Oct 2021 • Yucai Fan, Yuhang Yao, Carlee Joe-Wong
These works, however, do not fully address the challenge of flexibly assigning different importance to snapshots of the graph at different times, which depending on the graph dynamics may have more or less predictive power on the labels.
2 code implementations • 16 Dec 2020 • Yuhang Yao, Carlee Joe-Wong
We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters.