Timezone: »
In federated learning, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round. Naive weighted aggregation of such models causes objective inconsistency, that is, the global model converges to a stationary point of a mismatched objective function which can be arbitrarily different from the true objective. This paper provides a general framework to analyze the convergence of federated heterogeneous optimization algorithms. It subsumes previously proposed methods such as FedAvg and FedProx and provides the first principled understanding of the solution bias and the convergence slowdown due to objective inconsistency. Using insights from this analysis, we propose FedNova, a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence.
Author Information
Jianyu Wang (Carnegie Mellon University)
Qinghua Liu (Princeton University)
Hao Liang (Carnegie Mellon University)
Gauri Joshi (Carnegie Mellon University)
H. Vincent Poor (Princeton University)
More from the Same Authors
-
2021 Spotlight: Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms »
Chi Jin · Qinghua Liu · Sobhan Miryoosefi -
2022 : Federated Learning under Distributed Concept Drift »
Ellango Jothimurugesan · Kevin Hsieh · Jianyu Wang · Gauri Joshi · Phillip Gibbons -
2023 Poster: Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL »
Qinghua Liu · Gellért Weisz · András György · Chi Jin · Csaba Szepesvari -
2023 Poster: Is RLHF More Difficult than Standard RL? »
Yuanhao Wang · Qinghua Liu · Chi Jin -
2023 Poster: Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations »
Minshuo Chen · Yu Bai · H. Vincent Poor · Mengdi Wang -
2023 Poster: Correlation Aware Distributed Vector Mean Estimation »
Shuli Jiang · PRANAY SHARMA · Gauri Joshi -
2023 Poster: Context-lumpable stochastic bandits »
Chung-Wei Lee · Qinghua Liu · Yasin Abbasi Yadkori · Chi Jin · Tor Lattimore · Csaba Szepesvari -
2023 Workshop: Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023 (FL@FM-NeurIPS'23) »
Jinghui Chen · Lixin Fan · Gauri Joshi · Sai Praneeth Karimireddy · Stacy Patterson · Shiqiang Wang · Han Yu -
2022 : To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning »
Yae Jee Cho · Divyansh Jhunjhunwala · Tian Li · Virginia Smith · Gauri Joshi -
2022 Workshop: Federated Learning: Recent Advances and New Challenges »
Shiqiang Wang · Nathalie Baracaldo · Olivia Choudhury · Gauri Joshi · Peter Richtarik · Praneeth Vepakomma · Han Yu -
2022 Poster: Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting »
Yuzhou Chen · Yulia Gel · H. Vincent Poor -
2022 Poster: Sample-Efficient Reinforcement Learning of Partially Observable Markov Games »
Qinghua Liu · Csaba Szepesvari · Chi Jin -
2022 Poster: Policy Optimization for Markov Games: Unified Framework and Faster Convergence »
Runyu Zhang · Qinghua Liu · Huan Wang · Caiming Xiong · Na Li · Yu Bai -
2021 Poster: Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation »
Divyansh Jhunjhunwala · Ankur Mallick · Advait Gadhikar · Swanand Kadhe · Gauri Joshi -
2021 Poster: Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms »
Chi Jin · Qinghua Liu · Sobhan Miryoosefi -
2020 Poster: Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters »
Kaiyi Ji · Jason Lee · Yingbin Liang · H. Vincent Poor -
2020 Poster: Sample-Efficient Reinforcement Learning of Undercomplete POMDPs »
Chi Jin · Sham Kakade · Akshay Krishnamurthy · Qinghua Liu -
2020 Spotlight: Sample-Efficient Reinforcement Learning of Undercomplete POMDPs »
Chi Jin · Sham Kakade · Akshay Krishnamurthy · Qinghua Liu -
2019 : Lunch break and poster »
Felix Sattler · Khaoula El Mekkaoui · Neta Shoham · Cheng Hong · Florian Hartmann · Boyue Li · Daliang Li · Sebastian Caldas Rivera · Jianyu Wang · Kartikeya Bhardwaj · Tribhuvanesh Orekondy · YAN KANG · Dashan Gao · Mingshu Cong · Xin Yao · Songtao Lu · JIAHUAN LUO · Shicong Cen · Peter Kairouz · Yihan Jiang · Tzu Ming Hsu · Aleksei Triastcyn · Yang Liu · Ahmed Khaled Ragab Bayoumi · Zhicong Liang · Boi Faltings · Seungwhan Moon · Suyi Li · Tao Fan · Tianchi Huang · Chunyan Miao · Hang Qi · Matthew Brown · Lucas Glass · Junpu Wang · Wei Chen · Radu Marculescu · tomer avidor · Xueyang Wu · Mingyi Hong · Ce Ju · John Rush · Ruixiao Zhang · Youchi ZHOU · Françoise Beaufays · Yingxuan Zhu · Lei Xia -
2019 Poster: Nonconvex Low-Rank Symmetric Tensor Completion from Noisy Data »
Changxiao Cai · Gen Li · H. Vincent Poor · Yuxin Chen -
2018 : Posters (all accepted papers) + Break »
Jianyu Wang · Denis Gudovskiy · Ziheng Jiang · Michael Kaufmann · Andreea Anghel · James Bradbury · Nikolas Ioannou · Nitin Agrawal · Emma Tosch · Gyeongin Yu · Keno Fischer · Jarrett Revels · Giuseppe Siracusano · Yaoqing Yang · Jeff Johnson · Yang You · Hector Yuen · Chris Ying · Honglei Liu · Nikoli Dryden · Xiangxi Mo · Yangzihao Wang · Amit Juneja · Micah Smith · Qian Yu · pramod gupta · Deepak Narayanan · Keshav Santhanam · Tim Capes · Abdul Dakkak · Norman Mu · Ke Deng · Liam Li · Joao Carreira · Luis Remis · Deepti Raghavan · Una-May O'Reilly · Amanpreet Singh · Mahmoud (Mido) Assran · Eugene Wu · Eytan Bakshy · Jinliang Wei · Michael Innes · Viral Shah · Haibin Lin · Conrad Sanderson · Ryan Curtin · Marcus Edel