SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead (1910.01355v4)

Abstract: Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the unreliable nature of end devices while the cost of device-server communication cannot be neglected. In this paper, we propose SAFA, a semi-asynchronous FL protocol, to address the problems in federated learning such as low round efficiency and poor convergence rate in extreme conditions (e.g., clients dropping offline frequently). We introduce novel designs in the steps of model distribution, client selection and global aggregation to mitigate the impacts of stragglers, crashes and model staleness in order to boost efficiency and improve the quality of the global model. We have conducted extensive experiments with typical machine learning tasks. The results demonstrate that the proposed protocol is effective in terms of shortening federated round duration, reducing local resource wastage, and improving the accuracy of the global model at an acceptable communication cost.

• The paper introduces SAFA, a semi-asynchronous federated averaging protocol that tolerates lag and enhances global model convergence efficiency.
• It utilizes post-training client selection to decouple server dependency from client availability, thereby boosting update effectiveness.
• The protocol employs cache-based discriminative aggregation to reduce communication overhead and minimize resource wastage in federated learning systems.

Overview of SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead

SAFA (Semi-Asynchronous Federated Averaging) is proposed to address efficiency and convergence challenges in Federated Learning (FL) systems, especially considering the unreliable nature of end devices and the costs associated with device-server communication. FL acknowledges the increasing demand for decentralized machine learning models that respect data privacy while requiring minimal data movement from distributed edge devices. However, traditional approaches face several obstacles, including communication overheads and device unreliability, which impedes efficiency and reduces model quality.

Key Contributions

SAFA integrates features from asynchronous machine learning to mitigate straggler impacts, model staleness, and client crashes, aiming to speed up the convergence of the global model. The primary contributions can be distilled into the following aspects:

1. Lag-Tolerant Model Distribution: SAFA adopts a lag-tolerant approach, distinguishing clients based on model versioning into up-to-date, deprecated, and tolerable categories. This approach cleverly allows asynchronous progress from clients while ensuring staleness does not overwhelm the global model's integrity, aiming to balance learning efficacy with communication costs. The parameter lag tolerance is introduced to control the allowance for out-of-sync client updates.
2. Post-Training Client Selection: Departing from conventional pre-selection strategies, SAFA employs a post-training selection methodology. By allowing devices to participate opportunistically, it decouples the server from client availability dynamics, thus optimizing the Effective Update Ratio (EUR) without pre-determined participation thresholds. The selection process prioritizes clients that have previously been less involved, helping to attenuate client involvement bias within the federated ecosystem.
3. Discriminative Aggregation with Cache Utilization: SAFA employs a three-step aggregation process which leverages a cache to heuristically enhance update selection and implicitly guides convergence through bypassing certain updates while inducing minimal resource wastage.

Experimental Results

The experimental analysis is conducted across different machine learning tasks, each simulating various network environments with variable client reliability. The results substantiate SAFA's capabilities in:

• Achieving higher accuracy of the global model in a fraction of traditional round lengths, thereby significantly reducing the temporal burden of federated training.
• Minimizing communication overhead, evidenced by sustainable synchronization ratios across diverse contexts. Lag-tolerant updates ensure communication costs are balanced with model convergence aspirations.
• Lowering local resource wastage, which is highlighted through the reduced futility rate in resource-constrained federated environments.

Implications and Future Directions

The implications of SAFA are multifaceted, spanning improved resource allocation in federated settings and better learning efficiency under typical IoT conditions characterized by unreliable clients. The protocol's adaptation suggests potentials for economizing communication bandwidth and computing resources, thus improving FL deployment viability in real-world applications.

Future work should consider extending SAFA to incorporate model parallelism, which holds promise for mitigating computational bottlenecks in federated contexts by allowing concurrent model execution. Additionally, enhancing model compression techniques could further alleviate communication burdens, reinforcing SAFA's utility in both constrained and large-scale deployments.

Conclusion

SAFA emerges as a robust contribution to the federated learning corpus, advancing the field by enabling better model convergence and efficiency amidst unreliable client participation. By bridging semi-asynchronous techniques with practical parameterization strategies, the protocol lessens the communication burden while leveraging client contributions more effectively, sustaining model accuracy in various deployment scales and operating environments.