Yutong Dai

Memory-adaptive Depth-wise Heterogeneous Federated Learning

2023 · arXiv (Cornell University)

Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devices in federated learning, such as mobile phones and IoT …

Variational Bayes for Federated Continual Learning

2024 · arXiv (Cornell University)

Federated continual learning (FCL) has received increasing attention due to its potential in handling real-world streaming data, characterized by evolving data distributions and varying client classes over time. The constraints of storage limitations and privacy …

Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions

2025 · arXiv (Cornell University)

The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerging paradigm: collaborative learning between on-device small model and cloud-based large model, …