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Found 6 papers, 2 papers with code
NTIRE 2025 Image Shadow Removal Challenge Report
This work examines the findings of the NTIRE 2025 Shadow Removal Challenge.
GAS: Generative Activation-Aided Asynchronous Split Federated Learning
To address this issue, we consider an asynchronous SFL framework, where an activation buffer and a model buffer are embedded on the server to manage the asynchronously transmitted activations and client-side models, respectively.
SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments
Split Federated Learning (SFL) is a distributed machine learning framework which strategically divides the learning process between a server and clients and collaboratively trains a shared model by aggregating local models updated based on data from distributed clients.
Asynchronous Wireless Federated Learning with Probabilistic Client Selection
Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server.
Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation
From an evaluation perspective, we build a benchmark to judge ancient Chinese translation quality in different scenarios and evaluate the ancient Chinese translation capacities of various existing models.
Client Selection for Federated Bayesian Learning
Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametric distributed learning framework for federated Bayesian learning, where multiple clients jointly train a machine learning model by communicating a number of non-random and interacting particles with the server.
