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Found 11 papers, 1 papers with code
Universal Auto-encoder Framework for MIMO CSI Feedback
Existing auto-encoder (AE)-based channel state information (CSI) frameworks have focused on a specific configuration of user equipment (UE) and base station (BS), and thus the input and output sizes of the AE are fixed.
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
To address these challenges, we leverage Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites.
Secure Aggregation for Buffered Asynchronous Federated Learning
A buffered asynchronous training protocol known as FedBuff has been proposed recently which bridges the gap between synchronous and asynchronous training to mitigate stragglers and to also ensure privacy simultaneously.
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning
We also demonstrate that, unlike existing schemes, LightSecAgg can be applied to secure aggregation in the asynchronous FL setting.
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users.
On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
In this work, we empirically show that the square function is not the best degree-2 polynomial that can replace the ReLU function even when restricting the polynomials to have integer coefficients.
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from the other parties.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Byzantine-Resilient Secure Federated Learning
This presents a major challenge for the resilience of the model against adversarial (Byzantine) users, who can manipulate the global model by modifying their local models or datasets.
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users.
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
How to train a machine learning model while keeping the data private and secure?
