Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters (2506.15825v1)

Abstract: In this paper, we first propose a novel algorithm for model fusion that leverages Wasserstein barycenters in training a global Deep Neural Network (DNN) in a distributed architecture. To this end, we divide the dataset into equal parts that are fed to "agents" who have identical deep neural networks and train only over the dataset fed to them (known as the local dataset). After some training iterations, we perform an aggregation step where we combine the weight parameters of all neural networks using Wasserstein barycenters. These steps form the proposed algorithm referred to as FedWB. Moreover, we leverage the processes created in the first part of the paper to develop an algorithm to tackle Heterogeneous Federated Reinforcement Learning (HFRL). Our test experiment is the CartPole toy problem, where we vary the lengths of the poles to create heterogeneous environments. We train a deep Q-Network (DQN) in each environment to learn to control each cart, while occasionally performing a global aggregation step to generalize the local models; the end outcome is a global DQN that functions across all environments.

Analysis of "Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters"

This paper introduces a novel approach to federated reinforcement learning (FRL), leveraging the concept of Wasserstein barycenters for model fusion to enhance the performance of distributed deep learning and federated neural networks. The authors primarily focus on developing the FedWB algorithm to improve the training efficacy of models in heterogeneous environments. The research is anchored in addressing federated reinforcement learning, particularly in settings where the working environments significantly vary across agents.

The proposed methodology centers on the aggregation of neural network weights across distributed agents using Wasserstein barycenters instead of traditional arithmetic averaging, as in prior work such as the Federated Averaging algorithm. This shift in methodology is significant due to the ability of Wasserstein barycenters to retain more geometric information during averaging processes, potentially providing robustness in handling diverse environmental conditions within the federated framework.

Contributions and Results

The paper extends its contributions across two major domains: distributed neural network training on the MNIST dataset and federated reinforcement learning for a heterogeneous setup using the CartPole problem. The results demonstrate that FedWB can maintain high accuracy in a federated setting compared to individual and Federated Averaging methods, especially when examining the early stages of training.

1. Distributed MNIST Classification:
   • The research outlines the benefits of parallelizing neural network training across multiple agents, showing an efficient convergence rate when applying the FedWB algorithm. The results depict a nuanced interaction between agent count and epoch requirement, suggesting that data distribution among agents significantly impacts learning efficiency.
   • The time-to-reach target accuracy assessments indicate theoretical improvements in processing speed when scaling the number of agents, balanced against potential communication overheads.
2. Heterogeneous Federated Reinforcement Learning:
   • In applying FedWB to train a global deep Q-network (DQN) for the CartPole task in varied environments, the authors highlight the algorithm's ability to accommodate different environmental settings effectively.
   • Comparative analysis displays that FedWB provides improved initial training acceleration as opposed to FedAvg, though both approaches eventually converge toward similar performance metrics.

Implications and Future Developments

The introduction of Wasserstein barycenters to federated learning architectures demonstrates potential in advancing models that must function across heterogeneous environments, especially where privacy and decentralized data processing are pivotal. While FedWB underscores initial advantages in model accuracy and convergence, the computational intensity of solving optimal transport problems suggests that this approach could benefit significantly from advancements in computational optimization techniques.

In practical applications, particularly in fields requiring real-time learning and adaptation such as autonomous driving and decentralized IoT systems, the implications are profound. Future exploration could delve into hybrid approaches that leverage the accuracy of Wasserstein barycenters in the early epochs and benefit from the speed of FedAvg in later stages of training. Additionally, optimization of the computational load during the aggregation step could further enhance the applicability of FedWB in resource-constrained environments.

Overall, the paper makes a substantive contribution to the field of federated learning, outlining a path toward more sophisticated, adaptable, and resilient learning systems. The theoretical considerations and experimental results provided not only augment current methods but also set the stage for subsequent research that builds on these insights to tackle other complex, real-world federated learning challenges.