Federated Learning with Adaptive Aggregated Gradients for Improved Performance and Convergence

Concetti Chiave

An adaptive federated learning framework that leverages aggregated gradients and decentralized learning rates to enhance model performance and convergence under heterogeneous data distributions.

Sintesi

The paper proposes an adaptive federated learning (FL) framework called FedAgg that addresses the challenges of client heterogeneity and slow convergence in traditional FL methods. The key innovations are:

1. Adaptive Learning Rate: FedAgg introduces an adaptive learning rate for each client, which is determined by considering the aggregated gradients of all clients and the deviation between the local and average model parameters. This helps alleviate the negative impact of client drifting and data heterogeneity.

2. Mean-Field Estimation: Since clients cannot directly access each other's local information during training, FedAgg introduces two mean-field terms to estimate the average of local gradients and parameters. This allows each client to independently compute its optimal adaptive learning rate without requiring explicit communication.

3. Theoretical Analysis: The authors provide a rigorous theoretical analysis to prove the existence of the mean-field terms and the convergence of the proposed FedAgg algorithm. They derive a closed-form expression for the adaptive learning rate and establish an upper bound on the convergence rate.

4. Empirical Evaluation: Extensive experiments on various datasets demonstrate that FedAgg outperforms state-of-the-art FL methods in terms of model performance and convergence speed, under both IID and non-IID data distributions.

The FedAgg framework effectively addresses the challenges of client heterogeneity and slow convergence in federated learning by introducing an adaptive learning rate mechanism and leveraging mean-field theory to estimate global statistics without direct communication. The theoretical analysis and empirical results showcase the superiority of the proposed approach.

Statistiche

The local gradient function ∇𝐹𝑖(𝒘) has P-bounded gradients, i.e., ∥∇𝐹𝑖(𝒘𝒕
𝒊,𝒍)∥≤𝑃.
The global loss function 𝐹(𝒘) satisfies 𝛽-Lipschitz continuous and Polyak-Łojasiewicz condition.
The local model parameter ∥𝒘𝒕
𝒊,𝒍∥is bounded, i.e., ∥𝒘𝒕
𝒊,𝒍∥≤𝑄.

Citazioni

"To surmount the obstacle that acquiring other clients' local information, we introduce the mean-field approach by leveraging two mean-field terms to approximately estimate the average local parameters and gradients over time in a manner that precludes the need for local information exchange among clients and design the decentralized adaptive learning rate for each client."
"Through meticulous theoretical analysis, we provide a robust convergence guarantee for our proposed algorithm and ensure its wide applicability."

Approfondimenti chiave tratti da

FedAgg: Adaptive Federated Learning with Aggregated Gradients

by Wenhao Yuan,... alle arxiv.org 04-15-2024

https://arxiv.org/pdf/2303.15799.pdf

Domande più approfondite

How can the FedAgg framework be extended to handle dynamic client participation, where clients may join or leave the federated network during training

To extend the FedAgg framework to handle dynamic client participation, where clients may join or leave the federated network during training, several adjustments can be made. One approach is to implement a dynamic client selection mechanism that can adapt to changes in the network composition. This mechanism can involve periodically evaluating the performance and contribution of each client and adjusting the selection criteria accordingly. Clients that join the network can go through a pre-training phase to synchronize with the current global model, while clients that leave can have their contributions gradually phased out to minimize disruption to the training process. Additionally, communication protocols can be enhanced to accommodate the dynamic nature of client participation, ensuring seamless integration and disconnection of clients without compromising the overall training progress.

What are the potential implications of the FedAgg approach on the fairness and privacy aspects of federated learning, and how can these be further investigated

The FedAgg approach has significant implications for fairness and privacy in federated learning. On the fairness front, the adaptive learning rate mechanism in FedAgg can help mitigate the impact of client heterogeneity, ensuring that all participants have an equal opportunity to contribute to the global model. This can lead to more balanced model performance across all clients, promoting fairness in the training process. Regarding privacy, the mean-field estimation technique used in FedAgg can help protect sensitive client data by reducing the need for direct exchange of local information among clients. However, further investigation is needed to assess the robustness of the privacy-preserving mechanisms in FedAgg, including analyzing potential vulnerabilities and exploring additional privacy-enhancing techniques such as differential privacy or secure aggregation protocols.

Can the mean-field estimation technique used in FedAgg be applied to other distributed optimization problems beyond federated learning

The mean-field estimation technique employed in FedAgg can indeed be applied to other distributed optimization problems beyond federated learning. The concept of approximating global parameters and gradients using mean-field terms can be beneficial in scenarios where direct communication or sharing of local information is restricted. For example, in decentralized optimization tasks involving multiple autonomous agents or IoT devices, mean-field estimation can enable efficient coordination and decision-making without compromising individual data privacy. By adapting the FedAgg approach and mean-field estimation to different distributed optimization contexts, researchers can explore novel applications in areas such as edge computing, sensor networks, and multi-agent systems, enhancing the scalability and performance of decentralized algorithms.