Conferences

  1. Haibo Yang, Peiwen Qiu, and Jia Liu, Taming Fat-Tailed (``Heavier-Tailed" with Potentially Infinite Variance) Noise in Federated Learning, in Proc. NeurIPS, New Orleans, LA, Dec. 2022 (acceptance rate: 25.6%)
  2. Haibo Yang, Zhuqing Liu, Xin Zhang, and Jia Liu, SAGDA: Achieving O(ε-2) Communication Complexity in Federated Min-Max Learning, in Proc. NeurIPS, New Orleans, LA, Dec. 2022 (acceptance rate: 25.6%)
  3. Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, and Zhengyuan Zhu, NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data, in Proc. ACM MobiHoc, Seoul, South Korea, Oct. 2022 (acceptance rate: 19.8%).
  4. Haibo Yang, Xin Zhang, Prashant Khanduri, and Jia Liu, Anarchic Federated Learning, in Proc. ICML, Baltimore, MD, July 2022 (Long Presentation, long presentation rate: 2%, acceptance rate: 21.9%).
  5. Jiayu Mao*, Haibo Yang*, Peiwen Qiu, Jia Liu, and Aylin Yener, CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks, in Proc. IEEE SPAWC, Oulu, Finland, June 2022. (equal contribution)
  6. Haibo Yang, Peiwen Qiu, Jia Liu, and Aylin Yener, Over-the-Air Federated Learning With Joint Adaptive Computation and Power Control, in Proc. IEEE ISIT, Espoo, Finland, June 2022.
  7. Prashant Khanduri, Haibo Yang, Mingyi Hong, Jia Liu, Hoi To Wai, and Sijia Liu, Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach, in Proc. ICLR, Virtual Event, April 2022 (acceptance rate: 32%).
  8. Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, and Pramod Varshney, STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning, in Proc. NeurIPS, Virtual Event, Dec 2021 (acceptance rate: 26%).
  9. Haibo Yang, Jia Liu, and Elizabeth S. Bentley, CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning, in Proc. IEEE/IFIP WiOpt, Virtual Event, Oct. 2021.
  10. Haibo Yang, Minghong Fang, and Jia Liu Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning, in Proc. ICLR, Virtual Event, May 2021 (acceptance rate: 28.6%).
  11. Haibo Yang, Xin Zhang, Minghong Fang, and Jia Liu Adaptive Multi-Hierarchical signSGD for Communication-Efficient Distributed Optimization, in Proc. IEEE SPAWC, Atlanta, GA, May 2020.
  12. Haibo Yang, Xin Zhang, Minghong Fang, and Jia Liu Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach, in Proc. IEEE CDC, Nice, France, Dec. 2019.