Combating Client Dropouts in Federated Learning by Mimicking Central Updates

Centrala begrepp

The core message of this paper is to propose a novel federated learning algorithm named MimiC that can effectively combat the negative impacts of arbitrary client dropouts by modifying the received model updates to mimic an imaginary central update.

Sammanfattning

The paper investigates the convergence performance of the classical FedAvg algorithm in the presence of arbitrary client dropouts. It is found that client dropouts introduce a biased update in each training iteration, and with the common choice of a decaying learning rate, the model learned by FedAvg may oscillate around a stationary point of the global loss function in the worst case.

To address this issue, the authors propose a novel federated learning algorithm named MimiC. The key idea of MimiC is to augment each received model update from the active clients with a correction variable derived from previous iterations, in order to mimic an imaginary central update. This correction is performed at the server side and introduces no additional computation or communication overhead to the clients.

The theoretical analysis shows that despite the modified update in MimiC still being a biased estimate of the global gradient, its divergence is bounded and diminishes to zero with a proper choice of the learning rates. The convergence of MimiC is further characterized by imposing a mild assumption on the maximum number of consecutive client dropout iterations. It is also shown that MimiC converges with a high probability when clients drop out in a probabilistic pattern.

The extensive simulation results validate the convergence of MimiC with client dropouts in different scenarios and demonstrate that MimiC consistently produces better models than the baseline methods.

Statistik

The global training objective is to minimize the average loss over all clients' local datasets.
The local learning rate satisfies ηL ≤ 1/10L, where L is the smoothness constant.
There exists a constant τmax such that each client can be inactive for at most τmax consecutive iterations.

Citat

"Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server."
"However, when being deployed at mobile edge networks, clients may have unpredictable availability and drop out of the training process, which hinders the convergence of FL."

Viktiga insikter från

MimiC

by Yuchang Sun,... på arxiv.org 04-09-2024

https://arxiv.org/pdf/2306.12212.pdf

Djupare frågor

How can the proposed MimiC algorithm be extended to handle more complex client dropout patterns, such as correlated dropouts across clients

To extend the MimiC algorithm to handle more complex client dropout patterns, such as correlated dropouts across clients, we can introduce a mechanism to capture and model the dependencies between client availability. By incorporating a probabilistic model that accounts for the correlations in dropout events among clients, we can adjust the correction variables in MimiC based on the observed patterns of dropout occurrences. This adaptation would involve analyzing the historical dropout data to identify and exploit the correlations, allowing for more accurate correction of the model updates in the presence of correlated client dropouts.

How can the MimiC algorithm be adapted to address the challenges in cross-silo federated learning settings, where the clients have more stable connectivity but potentially more heterogeneous data distributions

Adapting the MimiC algorithm to address the challenges in cross-silo federated learning settings involves considering the differences in client characteristics and data distributions compared to cross-device FL. In cross-silo FL, where clients are typically larger entities with more stable connectivity but potentially more heterogeneous data distributions, the MimiC algorithm can be modified to account for the varying levels of data heterogeneity and client resources. This adaptation may involve adjusting the correction variables based on the specific characteristics of the clients in the cross-silo setting, such as their computational capabilities, data volume, and network reliability. By tailoring the correction mechanism to the unique aspects of cross-silo FL, MimiC can effectively handle the challenges posed by the different client environment.

What are the potential applications of the MimiC algorithm beyond federated learning, such as in distributed optimization or decentralized learning systems

The MimiC algorithm, with its focus on mitigating the impact of client dropouts and data heterogeneity in federated learning, has potential applications beyond FL. One such application is in distributed optimization scenarios where multiple nodes collaborate to solve optimization problems while facing challenges such as communication constraints, unreliable nodes, and non-IID data distributions. By adapting the principles of MimiC to distributed optimization settings, the algorithm can help improve the convergence and robustness of optimization algorithms in distributed environments. Additionally, MimiC can be applied to decentralized learning systems, where multiple autonomous agents aim to collaboratively learn a shared model while preserving data privacy and dealing with communication constraints. By leveraging the correction mechanism of MimiC to account for the decentralized nature of the learning process, the algorithm can enhance the efficiency and effectiveness of decentralized learning systems.