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Found 8 papers, 0 papers with code
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy
In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy.
Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking
On the other hand, the edge-based FL framework that relies on an edge server co-located with mobile base station for model aggregation has low communication latency but suffers from degraded model accuracy due to the limited coverage of edge server.
Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy
Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great attention recently and can protect privacy in comparison with the traditional centralized learning paradigm.
Hybrid Local SGD for Federated Learning with Heterogeneous Communications
Communication is a key bottleneck in federated learning where a large number of edge devices collaboratively learn a model under the orchestration of a central server without sharing their own training data.
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization
Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost.
Concentrated Differentially Private and Utility Preserving Federated Learning
Federated learning is a machine learning setting where a set of edge devices collaboratively train a model under the orchestration of a central server without sharing their local data.
Differentially Private Federated Learning for Resource-Constrained Internet of Things
With the proliferation of smart devices having built-in sensors, Internet connectivity, and programmable computation capability in the era of Internet of things (IoT), tremendous data is being generated at the network edge.
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
The goal of this paper is to provide differential privacy for ADMM-based distributed machine learning.
