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Advances in Robust Federated Learning: Heterogeneity Considerations
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 structures, task objectives, computational capabilities, and communication resources.
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
FedBRB can uses small local models to train all blocks of the large global model, and broadcasts the trained parameters to the entire space for faster information interaction.
Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform
By using the model watermarking technology, we point out the possibility of building a unified platform for model ownership verification.
Migrate Demographic Group For Fair GNNs
To tackle this problem, we propose a brand new framework, FairMigration, which can dynamically migrate the demographic groups instead of keeping that fixed with raw sensitive attributes.
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development.
Tensor Completion via Convolutional Sparse Coding Regularization
On the contrary, in the field of signal processing, Convolutional Sparse Coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data.
