CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning (2306.01207v1)

Abstract: Asynchronous federated learning aims to solve the straggler problem in heterogeneous environments, i.e., clients have small computational capacities that could cause aggregation delay. The principle of asynchronous federated learning is to allow the server to aggregate the model once it receives an update from any client rather than waiting for updates from multiple clients or waiting a specified amount of time in the synchronous mode. Due to the asynchronous setting, the stale model problem could occur, where the slow clients could utilize an outdated local model for their local data training. Consequently, when these locally trained models are uploaded to the server, they may impede the convergence of the global training. Therefore, effective model aggregation strategies play a significant role in updating the global model. Besides, client scheduling is also critical when heterogeneous clients with diversified computing capacities are participating in the federated learning process. This work first investigates the impact of the convergence of asynchronous federated learning mode when adopting the aggregation coefficient in synchronous mode. The effective aggregation solutions that can achieve the same convergence result as in the synchronous mode are then proposed, followed by an improved aggregation method with client scheduling. The simulation results in various scenarios demonstrate that the proposed algorithm converges with a similar level of accuracy as the classical synchronous federated learning algorithm but effectively accelerates the learning process, especially in its early stage.

• The paper introduces a novel framework that integrates adaptive client scheduling with dynamic model aggregation to effectively mitigate delays and staleness in asynchronous federated learning.
• It employs adjustable aggregation coefficients to balance contribution relevance and ensure timely global updates despite heterogeneous client capabilities.
• Simulations demonstrate accelerated convergence and comparable accuracy to synchronous methods, underscoring practical benefits for diverse FL applications in IoT and edge computing.

CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning

The paper "CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning" addresses key challenges in the domain of Federated Learning (FL), specifically focusing on asynchronous systems. Asynchronous Federated Learning (AFL) is increasingly critical for scenarios involving heterogeneous devices with varying computational capacities. The primary aim is to resolve issues related to delayed aggregation caused by slower clients, commonly referred to as "stragglers," and to mitigate the staleness problem affecting slow clients' model contributions.

Overview and Contributions

The paper begins by identifying the inherent challenges within classical Synchronous Federated Learning (SFL), where the aggregation of models is dependent on receiving updates from a fixed number of clients or after a predetermined time interval. This mode is prone to delays due to participation constraints, particularly in environments with devices of diverse computational capabilities.

In contrast, AFL enables the aggregation process to commence as soon as model updates from any client are available, thereby circumventing the delays introduced by stragglers. Nevertheless, AFL introduces the problem of model staleness, necessitating sophisticated aggregation techniques and client scheduling protocols to ensure prompt and accurate updating of the global model.

The authors propose an innovative framework named CSMAAFL, which integrates both client scheduling and model aggregation strategies to enhance AFL efficiency. Key contributions include:

1. Development of a New AFL Framework: This framework incorporates client scheduling that accounts for computational capabilities and fairness, while also tackling the staleness problem through model aggregation. A new architecture is proposed where each client can update the server without waiting for others, ensuring timely updates of the global model.
2. Aggregation Mechanism: The paper suggests adopting the aggregation coefficient from the synchronous model to maintain the accuracy level while adapting it to minimize staleness impact. By dynamically adjusting these coefficients, the solution maintains a balance between contribution and relevance of models received from clients during iterations.
3. Comparative Analysis: Simulation results demonstrate that the proposed CSMAAFL algorithm accelerates convergence and achieves a similar degree of accuracy as the SFL. Particularly, in the initial stages, the learning speed is enhanced without a commensurate increase in completion time.
4. Handling Variability in Client Computation Capabilities: The proposed client scheduling mechanism optimizes resource utilization by prioritizing clients based on model freshness and computational capability, enhancing both efficiency and equity among heterogeneous clients.

Implications and Future Directions

The proposed CSMAAFL framework presents significant theoretical advancements in asynchronous federated learning, particularly in handling the staleness issue effectively while improving the convergence rate of the learning process. Practically, this research could lead to better implementations of FL in environments characterized by device heterogeneity, such as IoT networks and edge computing infrastructures.

Future research could further refine the staleness mitigation strategies and explore more dynamic scheduling algorithms that adapt to real-time variations in client conditions and network states. Additionally, integrating more sophisticated optimization techniques for aggregation, such as those leveraging adaptive learning rates or personalized federated approaches, could further enhance the AFL frameworks' scalability and robustness.

In conclusion, the CSMAAFL framework offers a promising direction for AFL by effectively incorporating considerations of computational diversity and fairness in client scheduling and aggregation, providing a pathway to more efficient and practical federated learning implementations.