Efficient Cross-Silo Federated Learning with FedCompass

Concepts de base

FedCompass introduces a semi-asynchronous federated learning algorithm with a computing power-aware scheduler to address client heterogeneity and data disparities, achieving faster convergence and higher accuracy than other algorithms.

Résumé

FedCompass proposes an innovative approach to cross-silo federated learning, addressing issues of client heterogeneity and data disparities. By dynamically assigning varying training tasks based on computing power, it reduces model staleness and improves efficiency. The algorithm outperforms synchronous and asynchronous methods in terms of convergence speed and accuracy on non-IID datasets.

Key points:

• Introduction to Federated Learning (FL) and its challenges.
• Proposal of FedCompass for efficient cross-silo FL.
• Explanation of the Computing Power-Aware Scheduler.
• Comparison with other FL algorithms like FedAvg, FedAsync, and FedBuff.
• Convergence analysis showcasing the benefits of FedCompass.
• Experiment results demonstrating superior performance in various settings.

Stats

"FedCompass achieves faster convergence and higher accuracy than other asynchronous algorithms."
"The server assigns a minimum of Qmin local steps to all clients for warm-up."
"Client speed information is gathered and updated each time a client responds with the local update."

Citations

"FedCompass ensures that multiple locally trained models from clients are received almost simultaneously as a group for aggregation."
"Using diverse non-IID heterogeneous distributed datasets, we demonstrate that FedCompass achieves faster convergence and higher accuracy than other asynchronous algorithms."

Idées clés tirées de

FedCompass

by Zilinghan Li... à arxiv.org 03-12-2024

https://arxiv.org/pdf/2309.14675.pdf

Questions plus approfondies

How does FedCompass handle sudden changes in client computing power

FedCompass handles sudden changes in client computing power by dynamically assigning varying numbers of local training tasks to different clients based on their individual computing speeds. This adaptability ensures that multiple client models are received almost simultaneously for group aggregation, reducing the staleness of local models and minimizing waiting times. The Compass scheduler profiles the computing power of each client and adjusts the number of local steps assigned to ensure efficient global model updates without prolonged delays from straggler clients.

What implications does the dynamic assignment of local training tasks have on overall model performance

The dynamic assignment of local training tasks in FedCompass has significant implications on overall model performance. By grouping clients with similar computing power together for simultaneous global aggregation, FedCompass effectively reduces model staleness and minimizes client drift. This approach leads to faster convergence rates and higher accuracy compared to other asynchronous algorithms, while maintaining efficiency in federated learning settings with heterogeneous clients and non-IID datasets.

How can the concept of client drift be further mitigated in federated learning systems

To further mitigate client drift in federated learning systems, additional strategies can be implemented alongside FedCompass. One approach is to incorporate adaptive learning rate mechanisms that adjust the learning rate based on individual client characteristics or performance metrics during training rounds. Additionally, implementing robust outlier detection techniques can help identify and address potential issues caused by outliers or unreliable clients contributing noisy gradients during the training process. By continuously monitoring and adapting to variations in client behavior or data distribution, these strategies can enhance the stability and performance of federated learning systems while mitigating the impact of client drift.