Timezone: »
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Marco Bornstein · Tahseen Rabbani · Evan Wang · Amrit Bedi · Furong Huang
Event URL: https://openreview.net/forum?id=sakN4NI67sb »
The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchronization of client models where the speed of synchronization depends upon the slowest client. In this work, we propose SWIFT: a wait-free decentralized FL algorithm that allows clients to conduct training at their own speed. Theoretically, we prove that SWIFT matches the gold-standard convergence rate $\mathcal{O}(1/\sqrt{T})$ of parallel stochastic gradient descent for convex and non-convex smooth optimization (total iterations $T$). Furthermore, this is done in the IID and non-IID settings without any bounded-delay assumption for slow clients, which is required by other asynchronous decentralized FL algorithms. Although SWIFT achieves the same convergence rate with respect to $T$ as other state-of-the-art (SOTA) parallel stochastic algorithms, it converges faster with respect to time due to its wait-free structure. Our experimental results demonstrate that communication costs between clients in SWIFT fall by an order of magnitude compared to synchronous counterparts. Furthermore, SWIFT produces loss levels for image classification, over IID and non-IID data settings, upwards of 50\% faster than existing SOTA algorithms.
The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchronization of client models where the speed of synchronization depends upon the slowest client. In this work, we propose SWIFT: a wait-free decentralized FL algorithm that allows clients to conduct training at their own speed. Theoretically, we prove that SWIFT matches the gold-standard convergence rate $\mathcal{O}(1/\sqrt{T})$ of parallel stochastic gradient descent for convex and non-convex smooth optimization (total iterations $T$). Furthermore, this is done in the IID and non-IID settings without any bounded-delay assumption for slow clients, which is required by other asynchronous decentralized FL algorithms. Although SWIFT achieves the same convergence rate with respect to $T$ as other state-of-the-art (SOTA) parallel stochastic algorithms, it converges faster with respect to time due to its wait-free structure. Our experimental results demonstrate that communication costs between clients in SWIFT fall by an order of magnitude compared to synchronous counterparts. Furthermore, SWIFT produces loss levels for image classification, over IID and non-IID data settings, upwards of 50\% faster than existing SOTA algorithms.
Author Information
Marco Bornstein (University of Maryland)
Tahseen Rabbani (University of Maryland, College Park)
Evan Wang (California Institute of Technology)
Amrit Bedi (University of Maryland, College Park)
Furong Huang (University of Maryland)
More from the Same Authors
-
2021 : Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL »
Yanchao Sun · Ruijie Zheng · Yongyuan Liang · Furong Huang -
2021 : Efficiently Improving the Robustness of RL Agents against Strongest Adversaries »
Yongyuan Liang · Yanchao Sun · Ruijie Zheng · Furong Huang -
2022 : SMART: Self-supervised Multi-task pretrAining with contRol Transformers »
Yanchao Sun · shuang ma · Ratnesh Madaan · Rogerio Bonatti · Furong Huang · Ashish Kapoor -
2022 : Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning »
Souradip Chakraborty · Amrit Bedi · Alec Koppel · Furong Huang · Pratap Tokekar · Dinesh Manocha -
2022 : GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint »
Paiheng Xu · Yuhang Zhou · Bang An · Wei Ai · Furong Huang -
2022 : Controllable Attack and Improved Adversarial Training in Multi-Agent Reinforcement Learning »
Xiangyu Liu · Souradip Chakraborty · Furong Huang -
2022 : Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity »
Mucong Ding · Tahseen Rabbani · Bang An · Evan Wang · Furong Huang -
2022 : Faster Hyperparameter Search on Graphs via Calibrated Dataset Condensation »
Mucong Ding · Xiaoyu Liu · Tahseen Rabbani · Furong Huang -
2022 : DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations »
Eitan Borgnia · Jonas Geiping · Valeriia Cherepanova · Liam Fowl · Arjun Gupta · Amin Ghiasi · Furong Huang · Micah Goldblum · Tom Goldstein -
2022 : Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function »
Ruijie Zheng · Xiyao Wang · Huazhe Xu · Furong Huang -
2022 : Contributed Talk: Controllable Attack and Improved Adversarial Training in Multi-Agent Reinforcement Learning »
Xiangyu Liu · Souradip Chakraborty · Furong Huang -
2022 Spotlight: Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach »
Kaiwen Yang · Yanchao Sun · Jiahao Su · Fengxiang He · Xinmei Tian · Furong Huang · Tianyi Zhou · Dacheng Tao -
2022 Poster: Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability »
Roman Levin · Manli Shu · Eitan Borgnia · Furong Huang · Micah Goldblum · Tom Goldstein -
2022 Poster: Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity »
Mucong Ding · Tahseen Rabbani · Bang An · Evan Wang · Furong Huang -
2022 Poster: Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning »
Yongyuan Liang · Yanchao Sun · Ruijie Zheng · Furong Huang -
2022 Poster: End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking »
Arpit Bansal · Avi Schwarzschild · Eitan Borgnia · Zeyad Emam · Furong Huang · Micah Goldblum · Tom Goldstein -
2022 Poster: Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach »
Kaiwen Yang · Yanchao Sun · Jiahao Su · Fengxiang He · Xinmei Tian · Furong Huang · Tianyi Zhou · Dacheng Tao -
2022 Poster: Transferring Fairness under Distribution Shifts via Fair Consistency Regularization »
Bang An · Zora Che · Mucong Ding · Furong Huang -
2021 : Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL »
Yanchao Sun · Ruijie Zheng · Yongyuan Liang · Furong Huang -
2021 : Efficiently Improving the Robustness of RL Agents against Strongest Adversaries »
Yongyuan Liang · Yanchao Sun · Ruijie Zheng · Furong Huang