Search Results for author:
Found 8 papers, 2 papers with code
Heterogeneous Federated Learning with Splited Language Model
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradigms, to ensure model privacy while diminishing the resource overhead of each client, especially on large transformer models in a resource-constrained environment, e. g., Internet of Things (IoT).
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern.
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems.
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
However, it suffers from issues in terms of communication, resource of MTs, and privacy.
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise.
Make Landscape Flatter in Differentially Private Federated Learning
Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with better stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance.
Improving the Model Consistency of Decentralized Federated Learning
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its neighbors in a decentralized communication network.
Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplace
In this paper, we propose an offline counterfactual policy estimation framework called Genie to optimize Sponsored Search Marketplace.
