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Found 11 papers, 0 papers with code
FLASH: Federated Learning Across Simultaneous Heterogeneities
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data.
Plug-and-Play Transformer Modules for Test-Time Adaptation
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model.
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear.
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.
Sustainable Federated Learning
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems.
Energy-Harvesting Distributed Machine Learning
This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks.
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.
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?
