Collaborative Deep Learning in Fixed Topology Networks (1706.07880v1)

Abstract: There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes. While most advances focus on model parallelization and engaging multiple computing agents via using a central parameter server, aspect of data parallelization along with decentralized computation has not been explored sufficiently. In this context, this paper presents a new consensus-based distributed SGD (CDSGD) (and its momentum variant, CDMSGD) algorithm for collaborative deep learning over fixed topology networks that enables data parallelization as well as decentralized computation. Such a framework can be extremely useful for learning agents with access to only local/private data in a communication constrained environment. We analyze the convergence properties of the proposed algorithm with strongly convex and nonconvex objective functions with fixed and diminishing step sizes using concepts of Lyapunov function construction. We demonstrate the efficacy of our algorithms in comparison with the baseline centralized SGD and the recently proposed federated averaging algorithm (that also enables data parallelism) based on benchmark datasets such as MNIST, CIFAR-10 and CIFAR-100.

• The paper introduces consensus-based algorithms (CDSGD and CDMSGD) that enable decentralized deep learning without a central server.
• It employs Lyapunov function analysis to prove linear convergence for strongly convex functions and sublinear rates for diminishing step sizes.
• Empirical results on MNIST and CIFAR datasets show that CDMSGD achieves competitive accuracy with a lower generalization gap compared to FedAvg.

Collaborative Deep Learning in Fixed Topology Networks: An Overview

The paper "Collaborative Deep Learning in Fixed Topology Networks" presents novel algorithms aimed at enhancing deep learning scalability through distributed optimization strategies. Unlike conventional centralized methods, this research focuses on decentralized computation and data parallelization, advancing the collaborative training of deep learning models across multiple agents connected by a fixed communication network topology.

Key Contributions

The main contribution of this work is the introduction and analysis of two consensus-based algorithms: Consensus-based Distributed Stochastic Gradient Descent (CDSGD) and its momentum variant, Consensus-based Distributed Momentum SGD (CDMSGD). These algorithms enable both data parallelization and decentralized computation without relying on a central parameter server, distinguishing them from other approaches such as Downpour SGD, Elastic Averaging SGD, and FedAvg.

1. Algorithm Design: The CDSGD and CDMSGD algorithms incorporate consensus mechanisms to ensure coordinated updates across agents. This is particularly suitable for environments with communication constraints, such as IoT networks, where agents can only communicate with their direct neighbors.
2. Convergence Analysis: The researchers employ Lyapunov functions to rigorously analyze the convergence properties of the proposed algorithms. The analysis extends to both convex and non-convex optimization problems, with results demonstrating linear convergence rates for strongly convex functions under fixed step sizes and sublinear rates for diminishing step sizes.
3. Performance Evaluation: Empirical evaluations are conducted using standard datasets such as MNIST, CIFAR-10, and CIFAR-100. Results indicate that while CDSGD and CDMSGD may have slower convergence rates compared to centralized SGD, they achieve comparable or even superior accuracy in a decentralized setting. Notably, CDMSGD outperforms the Federated Averaging method in terms of final accuracy while maintaining a lower generalization gap.

Implications and Future Directions

The algorithms developed in this paper address significant challenges in distributed deep learning, particularly under fixed communication topologies that mimic real-world networking constraints. The decentralized nature of these methods facilitates privacy-preserving learning scenarios, which are becoming increasingly important in distributed systems involving sensitive data.

From a theoretical perspective, the application of Lyapunov-based analysis enriches the understanding of convergence dynamics in distributed optimization. Practically, the algorithms offer viable solutions for scalable training of deep networks in environments where central coordination is impractical or undesirable.

Future research directions may include extending these methods to handle extreme non-IID data distributions and dynamically evolving network topologies. Additionally, exploring adaptive learning rates that balance convergence speed and stability could further enhance the effectiveness of the proposed algorithms.

In conclusion, this paper makes significant advancements in distributed deep learning by offering robust algorithms that blend data parallelism with decentralized computation, thereby facilitating scalable and efficient model training across interconnected environments.