Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability in Dynamic Environments

Core Concepts

Federated Postponed Broadcast (FedPBC) algorithm converges to a stationary point of the non-convex global objective in the presence of stochastic and dynamic communication failures, without requiring any "balancedness" assumption on the uplink availability.

Abstract

The paper studies federated learning in the presence of stochastic and dynamic communication failures, where the uplink between the parameter server and client i is active with unknown probability pt i in round t. The authors first demonstrate that the widely adopted Federated Average (FedAvg) algorithm experiences significant bias when the pt i's vary across clients. To address this, they propose Federated Postponed Broadcast (FedPBC), a simple variant of FedAvg that postpones broadcasting the global model till the end of each round. The key insights are: FedPBC enables implicit gossiping among the clients with active links in each round, which helps bound the perturbation caused by non-uniform and time-varying pt i. FedPBC converges to a stationary point of the non-convex global objective, without requiring any "balancedness" assumption on pt i's or bounded stochastic gradients/noises. Extensive experiments on real-world datasets validate the analysis, showing FedPBC outperforms multiple baselines under diverse unreliable uplink patterns.

Stats

The paper does not provide any specific numerical data or statistics to support the key claims. The analysis is mostly theoretical, with numerical experiments conducted on real-world datasets to validate the performance of the proposed FedPBC algorithm.

Quotes

The paper does not contain any striking quotes that support the key logics.

Key Insights Distilled From

Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics

by Ming Xiang,S... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10091.pdf

Deeper Inquiries

What are the implications of the FedPBC algorithm for practical federated learning deployments in dynamic and unreliable network environments

The FedPBC algorithm presents significant implications for practical federated learning deployments in dynamic and unreliable network environments. By postponing the global model broadcasts until the end of each round, FedPBC enables implicit gossiping among clients with active links, mitigating the bias caused by non-uniform link activation probabilities. This approach allows the algorithm to converge to a stationary point of the non-convex global objective, even in the presence of stochastic uplink failures with unknown and arbitrary dynamics. In practical deployments, where communication links are often unreliable and subject to varying conditions, FedPBC offers a robust solution to ensure the convergence of federated learning models. The ability to handle dynamic communication failures and adapt to changing network environments makes FedPBC a valuable tool for scenarios where traditional federated learning algorithms may struggle to perform optimally. By bounding the perturbation caused by non-uniform and time-varying link activation probabilities, FedPBC enhances the stability and reliability of the learning process in federated environments.

How can the FedPBC algorithm be extended to handle other types of communication failures, such as downlink failures or partial participation

To extend the FedPBC algorithm to handle other types of communication failures, such as downlink failures or partial participation, several modifications and enhancements can be considered: Downlink Failures: For scenarios where downlink failures are prevalent, the FedPBC algorithm can be adapted to incorporate mechanisms for handling missing or delayed updates from the parameter server to the clients. By introducing error correction techniques or redundancy in the communication protocol, FedPBC can mitigate the impact of downlink failures on the convergence of the global model. Partial Participation: In cases where only a subset of clients participate in each round, FedPBC can be extended to dynamically adjust the aggregation weights based on the active clients. By incorporating adaptive strategies to account for varying levels of participation, FedPBC can optimize the model aggregation process and ensure efficient learning even with partial client involvement. By incorporating these enhancements, FedPBC can be tailored to address a wider range of communication failures and network dynamics, making it a versatile and adaptable algorithm for federated learning in diverse environments.

Can the techniques used in the FedPBC analysis be applied to other distributed optimization problems beyond federated learning

The techniques used in the FedPBC analysis, such as controlling information mixing errors and bounding the perturbation of global model dynamics, can be applied to other distributed optimization problems beyond federated learning. These techniques are fundamental in addressing challenges related to communication unreliability, network dynamics, and heterogeneous client participation, which are common in distributed optimization scenarios. For example, in distributed optimization tasks involving multiple agents with intermittent communication links, the methods employed in FedPBC can help ensure convergence to a consensus solution despite unreliable network conditions. By leveraging implicit gossiping and bounding perturbations, these techniques can enhance the robustness and efficiency of distributed optimization algorithms in dynamic and uncertain environments. Overall, the principles and strategies demonstrated in the FedPBC analysis can be generalized to various distributed optimization problems, providing insights and solutions for handling communication failures and network uncertainties in decentralized settings.