Federated learning (FL) systems face performance challenges in dealing with
heterogeneous devices and non-identically distributed data across clients. We
propose a dynamic global model aggregation method within Asynchronous Federated
Learning (AFL) deployments to address these issues. Our aggregation method
scores and adjusts the weighting of client model updates based on their upload
frequency to accommodate differences in device capabilities. Additionally, we
also immediately provide an updated global model to clients after they upload
their local models to reduce idle time and improve training efficiency. We
evaluate our approach within an AFL deployment consisting of 10 simulated
clients with heterogeneous compute constraints and non-IID data. The simulation
results, using the FashionMNIST dataset, demonstrate over 10% and 19%
improvement in global model accuracy compared to state-of-the-art methods
PAPAYA and FedAsync, respectively. Our dynamic aggregation method allows
reliable global model training despite limiting client resources and
statistical data heterogeneity. This improves robustness and scalability for
real-world FL deployments.

在非同步聯邦學習 (AFL) 的部署中，我們提出一種動態全球模型聚合方法，以應對異質設備和在客戶端之間非相同分佈的數據所帶來的性能挑戰。我們的聚合方法基於客戶端的上傳頻率對其模型更新的權重進行評分和調整，以適應設備能力的差異。此外，我們在客戶端上傳本地模型後立即提供更新的全球模型，以減少閒置時間並提高訓練效率。我們在由 10 個模擬客戶端組成的 AFL 部署中評估了我們的方法，這些客戶端具有異質的計算限制和非相同分佈的數據。使用 FashionMNIST 數據集的模擬結果顯示，與最先進的方法 PAPAYA 和 FedAsync 相比，全球模型的準確性分別提高了 10% 和 19%。我們的動態聚合方法即使在限制的客戶資源和統計數據異質性下也可以實現可靠的全球模型訓練，從而提高了現實世界的 FL 部署的韌性和可擴展性。