We investigate the hypothesis that exploring Federated Learning (FL) aggregation methods
can enhance training processes within FL frameworks, particularly in resource-constrained …

In Federated Learning (FL), the distributed nature and heterogeneity of client data present
both opportunities and challenges. While collaboration among clients can significantly …

Federated Learning (FL) has become a key choice for distributed machine learning. Initially
focused on centralized aggregation, recent works in FL have emphasized greater …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …

Federated learning (FL) is a machine learning system that allows a network of devices to
train a model without centralized data. This characteristic makes FL an ideal choice for …

In this position paper, we underscore the critical need for a systematic and structured
approach to comparing Federated Learning (FL) frameworks. Given the diversity of FL …

Federated learning is a cutting-edge method of model training, which leverages the end
users to train the global model on the server. The end users are responsible for training …

Federated Learning (FL) has emerged as a promising methodology for collaboratively
training machine learning models on decentralized devices. Notwithstanding, the effective …

Federated Learning (FL) is a distributed machine learning approach in which clients
contribute to learning a global model in a privacy preserved manner. Effective aggregation …

Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …