Adaptive Consensus Step-size for Decentralized Deep Learning with Communication Compression

Główne pojęcia

AdaGossip, a novel decentralized learning method, dynamically adjusts the consensus step-size based on the compressed model differences between neighboring agents to improve performance under constrained communication.

Streszczenie

The paper proposes AdaGossip, a novel decentralized learning algorithm that adaptively adjusts the consensus step-size based on the compressed model differences between neighboring agents. The key idea is that a higher error in the received neighbors' parameters due to compression requires a lower consensus step-size for that parameter. AdaGossip computes individual adaptive consensus step-size for different parameters from the estimates of second moments of the gossip-error.

The authors extend AdaGossip to decentralized machine learning, resulting in AdaG-SGD. Through extensive experiments on various datasets, model architectures, compressors, and graph topologies, the authors demonstrate that AdaG-SGD outperforms the current state-of-the-art CHOCO-SGD by 0-2% in test accuracy. The improvements are more prominent in larger graph structures and challenging datasets like ImageNet.

The paper also discusses the limitations of the proposed method, including the assumption of a doubly stochastic and symmetric mixing matrix, the need to tune the consensus step-size hyperparameter, and the additional memory and computation required to estimate the second raw moment of gossip-error.

Statystyki

Decentralized Stochastic Gradient Descent (DSGD) with Nesterov momentum achieves 88.39 ± 0.50% test accuracy on CIFAR-10 with a Dyck graph of 32 agents.
CHOCO-SGD achieves 86.71 ± 0.22% and 87.46 ± 0.28% test accuracy on CIFAR-10 with 90% top-k sparsification over Dyck and Torus graphs of 32 agents, respectively.
AdaG-SGD achieves 88.38 ± 0.12% and 88.36 ± 0.50% test accuracy on CIFAR-10 with 90% top-k sparsification over Dyck and Torus graphs of 32 agents, respectively.

Cytaty

"AdaGossip computes individual adaptive consensus step-size for different parameters from the estimates of second moments of the gossip-error."
"The exhaustive set of experiments on various datasets, model architectures, compressors, and graph topologies establish that the proposed AdaG-SGD improves the performance of decentralized learning with communication compression."

Kluczowe wnioski z

AdaGossip

by Sai Aparna A... o arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05919.pdf

Głębsze pytania

How can the proposed AdaGossip algorithm be extended to handle time-varying and directed graph topologies

To extend the AdaGossip algorithm to handle time-varying and directed graph topologies, we can incorporate techniques from stochastic gradient push (SGP). By combining AdaGossip with SGP, we can adapt the consensus step-size dynamically based on the changing topology and direction of communication links. This adaptation can involve updating the consensus step-size based on the varying weights associated with the edges in directed graphs and the evolving connectivity in time-varying graphs. Additionally, incorporating mechanisms to track the changes in the graph structure and adjusting the consensus step-size accordingly can enable AdaGossip to effectively handle time-varying and directed graph topologies.

What are the theoretical guarantees on the convergence rate of AdaGossip and AdaG-SGD

The theoretical guarantees on the convergence rate of AdaGossip and AdaG-SGD can be analyzed through rigorous mathematical proofs and convergence analysis. By leveraging tools from optimization theory and distributed algorithms, we can establish convergence guarantees for AdaGossip and AdaG-SGD. Specifically, we can analyze the convergence properties of the adaptive consensus step-size mechanism in AdaGossip, considering factors such as the impact of compression, graph topology, and model architecture on the convergence rate. Theoretical analysis can provide insights into the convergence behavior of the algorithms under different conditions and help validate their effectiveness in decentralized learning scenarios.

Can the adaptive consensus step-size mechanism be combined with other communication-efficient techniques like gradient tracking to further improve the performance of decentralized learning

The adaptive consensus step-size mechanism in AdaGossip can be combined with other communication-efficient techniques like gradient tracking to further enhance the performance of decentralized learning. By integrating gradient tracking with the adaptive consensus step-size, the algorithm can dynamically adjust the consensus rate based on the gradient information, leading to more efficient communication and faster convergence. This combination can improve the overall convergence speed and accuracy of decentralized learning algorithms by optimizing the communication process and leveraging the benefits of adaptive consensus step-size and gradient tracking simultaneously.