Federated Learning Based on Dynamic Regularization (2111.04263v2)

Abstract: We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to save transmission costs. We point out a fundamental dilemma, in that the minima of the local-device level empirical loss are inconsistent with those of the global empirical loss. Different from recent prior works, that either attempt inexact minimization or utilize devices for parallelizing gradient computation, we propose a dynamic regularizer for each device at each round, so that in the limit the global and device solutions are aligned. We demonstrate both through empirical results on real and synthetic data as well as analytical results that our scheme leads to efficient training, in both convex and non-convex settings, while being fully agnostic to device heterogeneity and robust to large number of devices, partial participation and unbalanced data.

• The paper proposes FedDyn, a federated learning method that uses dynamic regularization to align local and global optimizations.
• The paper provides rigorous convergence analysis across convex, strongly convex, and non-convex settings, outperforming methods like SCAFFOLD in heterogeneous environments.
• The paper demonstrates through experiments on datasets such as MNIST and CIFAR-10 that FedDyn significantly reduces communication rounds compared to traditional FL algorithms.

Federated Learning Based on Dynamic Regularization

This paper introduces a novel method for federated learning (FL) that leverages dynamic regularization to address communication inefficiencies inherent in distributed training. It revisits the FL framework through a communication lens, allowing more computations at the device level to minimize transmission costs. The proposed approach, named FedDyn, offers improved alignment between local and global optimization strategies without necessitating inexact minimizations typically employed by other methods.

Key Contributions

1. Dynamic Regularization: A unique aspect of FedDyn is its approach to device-level optimization. It incorporates a penalty term in each training round, aligning the local empirical loss minimization with the global empirical loss. This regularization ensures consistent stationary points between device and global optimization landscapes.
2. Convergence Analysis: The paper provides rigorous theoretical guarantees for FedDyn’s convergence in convex, strongly convex, and non-convex settings. Specifically, for convex functions, the convergence rate improves over existing methods like SCAFFOLD, especially under heterogeneous data distributions.
3. Empirical Validation: Experiments conducted across various datasets, including MNIST, EMNIST, CIFAR-10, CIFAR-100, and Shakespeare, demonstrate FedDyn's efficiency. It consistently reduces communication overhead compared to baselines such as FedAvg, FedProx, and SCAFFOLD, achieving target accuracies with fewer transmitted parameters.

Methodological Insights

• Communication Efficiency: FedDyn is designed to minimize communication rounds, which are a significant bottleneck in FL environments. By enabling a more substantial computation load on devices, it achieves better communication efficiency, crucial for bandwidth-constrained scenarios.
• Device Heterogeneity: The algorithm is robust to variations in device participation, data heterogeneity, and data imbalance, which are common challenges in real-world FL applications. Unlike methods that require extensive hyperparameter tuning to handle these scenarios, FedDyn’s regularization adapts dynamically, simplifying its deployment.

Comparative Analysis

The paper contrasts FedDyn’s performance with SCAFFOLD, highlighting a key conceptual difference: FedDyn does not transmit additional gradient states, reducing the bit-rate required per round. This reduction is particularly beneficial for applications demanding low-power consumption, such as IoT implementations.

Implications and Future Directions

The findings presented in this paper have significant implications for the development of efficient and scalable FL algorithms. By addressing the fundamental inconsistency between local and global loss minimization, FedDyn sets a foundation for more robust federated systems. As FL continues to evolve, integrating more advanced dynamic regularization techniques might further enhance model convergence and performance under diverse federated scenarios.

In future research, expanding the theoretical framework to encompass various network conditions and integrating compression techniques could be valuable. This advancement would align FedDyn more closely with practical deployments in heterogeneous networks, paving the way for broader adoption and innovation in federated learning.