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Found 9 papers, 0 papers with code
Analyzing the Impact of Partial Sharing on the Resilience of Online Federated Learning Against Model Poisoning Attacks
PSO-Fed reduces the communication load by enabling clients to exchange only a fraction of their model estimates with the server at each update round.
Efficient Knowledge Deletion from Trained Models through Layer-wise Partial Machine Unlearning
In this method, updates made to the model during training are pruned and stored, subsequently used to forget specific data from trained model.
Smoothing ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties
This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD).
Distributed Filtering Design with Enhanced Resilience to Coordinated Byzantine Attacks
Numerical results demonstrate the accuracy of the proposed BR-CDF and its robustness against Byzantine attacks.
Personalized Graph Federated Learning with Differential Privacy
Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy.
Asynchronous Online Federated Learning with Reduced Communication Requirements
The simulations reveal that in asynchronous settings, the proposed PAO-Fed is able to achieve the same convergence properties as that of the online federated stochastic gradient while reducing the communication overhead by 98 percent.
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
In this manner, we reduce the communication load of the participants and, therefore, render participation in the learning task more accessible.
Communication-Efficient Online Federated Learning Framework for Nonlinear Regression
As a solution, this paper presents a partial-sharing-based online federated learning framework (PSO-Fed) that enables clients to update their local models using continuous streaming data and share only portions of those updated models with the server.
Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm
The performance analysis of the proposed multi-task diffusion APA algorithm is studied in mean and mean square sense.
