Federated Optimization in Heterogeneous Networks
Tian Li
, Anit Kumar Sahu, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
MLSys 2020
[Arxiv]
[Code]
[Slides]
[Poster]
Fair Resource Allocation in Federated Learning
Tian Li
, Maziar Sanjabi, Ahmad Beirami, Virginia Smith
ICLR 2020
[PDF][Code]
Federated Learning: Challenges, Methods, and Future Directions
Tian Li
, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith
IEEE Signal Processing Magazine 2020
[Arxiv][Blog post]
Learning Context-aware Policies from Multiple Smart Homes via Federated Multi-Task Learning
Tianlong Yu, Tian Li, Yuqiong Sun, Susanta Nanda, Virginia Smith, Vyas Sekar, Srinivasan Seshan
IoTDI 2020
FedDANE: A Federated Newton-Type Method
Tian Li
, Anit Kumar Sahu, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith
Asilomar Conference on Signals, Systems and Computers 2019 (Invited Paper)
[Arxiv]
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li
, Jakub Konecny, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
Federated Learning Workshop @ NeurIPS 2019
[Website][Arxiv]
Ease.ml: Towards Multitenant Resource Sharing for Machine Learning Workloads
Tian Li
, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang
VLDB 2018
[PDF]
[Arxiv]
CUTE: Query Knowledge Graphs by Tabular Examples
Zichen Wang*, Tian Li*
, Yingxia Shao, Bin Cui
WAIM 2018 (demo)
[PDF]
[Poster]
An Overreaction to the Broken Machine Learning Abstraction: The ease.ml Vision
Ce Zhang, Wentao Wu, Tian Li
HILDA Workshop @ SIGMOD 2017
[PDF]
