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Found 5 papers, 2 papers with code
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models
Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices.
TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models
Our approach can compress the private functionalities of the large language model to lightweight slices and achieve the same level of protection as the shielding-whole-model baseline.
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML
These solutions, referred to as TEE-Shielded DNN Partition (TSDP), partition a DNN model into two parts, offloading the privacy-insensitive part to the GPU while shielding the privacy-sensitive part within the TEE.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning
This paper aims to improve the transfer performance from another angle - in addition to tuning the weights, we tune the structure of pre-trained models, in order to better match the target task.
