… We first describe our vertical asynchronous federated learning (VAFL) algorithm in a high
level as follows. During the learning process, from the server side, it waits until receiving a …

… Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

… We propose an asynchronous aggregation scheme for federated learning. We compare
its performance to the baseline synchronous federated-averaging algorithm [7]. We show its …

… • We propose FedBuff, a novel asynchronous federated optimization framework with
buffered asynchronous aggregation to achieve scalability and privacy against the honest-but-…

… develop an asynchronous online federated learning framework with … We propose an
asynchronous online federated learning … from lagging clients, and clients perform online learning …

… asynchronous FL framework, which well adapts to the heterogeneity of users, communication
environments and learning … validate that the proposed asynchronous FL framework can …

… The state-of-art solutions of federated learning are … gradient compression and asynchronous
federated learning with dual… correction to solve asynchronous federated learning problems. …

… The asynchronous scheme is proposed for FL. In the … we propose an adaptive asynchronous
federated learning (AAFL) … We design an adaptive asynchronous federated learning (AAFL) …

… asynchronous updating may seriously hurt training accuracy, especially on Non-IID data. In
this paper, we propose a semi-asynchronous federated learning … a federated learning system …

… models in federated learning by incorporating local differential privacy (LDP) into
gradient descent training scheme. 2) We develop a new asynchronous federated learning architecture …