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。

资源受限异步联邦学习系统中的降低系统偏差

Mitigating System Bias in Resource Constrained Asynchronous Federated  Learning Systems

We present a federated learning approach for learning a client adaptable,
robust model when data is non-identically and non-independently distributed
(non-IID) across clients. By simulating heterogeneous clients, we show that
adding learned client-specific conditioning improves model performance, and the
approach is shown to work on balanced and imbalanced data set from both audio
and image domains. The client adaptation is implemented by a conditional gated
activation unit and is particularly beneficial when there are large differences
between the data distribution for each client, a common scenario in federated
learning.

本论文提出了一种联邦学习方法，用于在数据在客户端之间是非独立和非同一分布的情况下，学习适应客户端的鲁棒性模型。通过模拟异构客户端，我们发现添加学习的客户端特定条件可以提高模型性能，并且该方法证明可以适用于音频和图像领域的平衡和不平衡数据集。客户端适应是通过条件门控激活单元实现的，并且在每个客户端的数据分布存在较大差异的情况下特别有益，这是联邦学习中常见的情况。