Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

Although remarkable progress has been made by existing federated learning (FL) platforms
to provide infrastructures for development, these platforms may not well tackle the …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

Federated learning (FL) facilitates distributed training across different IoT and edge devices,
safeguarding the privacy of their data. The inherent distributed structure of FL introduces …

As Large Language Models (LLMs) push the boundaries of AI capabilities, their demand for
data is growing. Much of this data is private and distributed across edge devices, making …

Numerous research studies in the field of federated learning (FL) have attempted to use
personalization to address the heterogeneity among clients, one of FL's most crucial and …

Research in the field of hyperparameter optimization (HPO) has been greatly accelerated by
existing HPO benchmarks. Nonetheless, existing efforts in benchmarking all focus on HPO …

As Internet users attach importance to their own privacy, and a number of laws and
regulations go into effect in most countries, Internet products need to provide users with …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

Federated multi-task learning (FMTL) has emerged as a promising framework for learning
multiple tasks simultaneously with client-aware personalized models. While the majority of …