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Found 18 papers, 4 papers with code
FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection
To prevent privacy leakages from gradients that are calculated on users' sensitive data, local differential privacy (LDP) has been considered as a privacy guarantee in federated SGD recently.
FLAME: Differentially Private Federated Learning in the Shuffle Model
In this work, by leveraging the \textit{privacy amplification} effect in the recently proposed shuffle model of differential privacy, we achieve the best of two worlds, i. e., accuracy in the curator model and strong privacy without relying on any trusted party.
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance
Predicting human intention is critical to facilitating safe and efficient human-robot collaboration (HRC).
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients.
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
In this way, all the clients can participate in the model learning in FL, and the final model can be big and powerful enough.
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the problem by training models over decentralized user data. However, a theoretically private solution in both the training and serving stages of federated recommendation is essential but still lacking. Furthermore, naively applying differential privacy (DP) to the two stages in federated recommendation would fail to achieve a satisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In this work, we propose a federated news recommendation method for achieving a better utility in model training and online serving under a DP guarantee. We first clarify the DP definition over behavior data for each round in the life-circle of federated recommendation systems. Next, we propose a privacy-preserving online serving mechanism under this definition based on the idea of decomposing user embeddings with public basic vectors and perturbing the lower-dimensional combination coefficients.
Double Robust Bayesian Inference on Average Treatment Effects
We propose a double robust Bayesian inference procedure on the average treatment effect (ATE) under unconfoundedness.
GUARD: A Safe Reinforcement Learning Benchmark
Due to the diversity of algorithms and tasks, it remains difficult to compare existing safe RL algorithms.
Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction and Robust Safe Control
Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors.
Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on singular formulas for individual or groups of robots.
A Lightweight and Transferable Design for Robust LEGO Manipulation
LEGO is a well-known platform for prototyping pixelized objects.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Coupling public and private gradient provably helps optimization
The success of large neural networks is crucially determined by the availability of data.
On the accuracy and efficiency of group-wise clipping in differentially private optimization
Recent advances have substantially improved the accuracy, memory cost, and training speed of differentially private (DP) deep learning, especially on large vision and language models with millions to billions of parameters.
Zero redundancy distributed learning with differential privacy
Deep learning using large models have achieved great success in a wide range of domains.
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems
On the one hand, to achieve model training in all the diverse clients, mobile computing systems can only use small low-performance models for collaborative learning.
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance
We prove that pre-projection enhances the convergence of DP-SGD by reducing the dependence of clipping error and bias to a fraction of the top gradient eigenspace, and in theory, limits cross-client variance to improve the convergence under heterogeneous federation.
Quasi-Bayesian Estimation and Inference with Control Functions
We consider a quasi-Bayesian method that combines a frequentist estimation in the first stage and a Bayesian estimation/inference approach in the second stage.
