3 accepted papers
Adopting pre-trained Vision-Language Models (VLMs) in Federated Learning (FL) presents a promising avenue for mitigating data scarcity and heterogeneity. However, existing solutions suffer from high computational complexity or ineffective knowledge aggregation. To address these problems, we propose …
Split Federated Learning (SFL) splits and collaboratively trains a shared model between clients and server, where clients transmit activations and client-side models to server for updates. Recent SFL studies assume synchronous transmission of activations and client-side models from clients to server…
Point cloud registration is a fundamental yet challenging task in computer vision and robotics. While framing it as a reconstruction problem has shown promise, traditional reconstruction approaches rely on positional encodings to encode positional information, which inadequately capture the intricat…