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Found 10 papers, 0 papers with code
Hierarchical Deep Double Q-Routing
This paper explores a deep reinforcement learning approach applied to the packet routing problem with high-dimensional constraints instigated by dynamic and autonomous communication networks.
Consistency Analysis of Replication-Based Probabilistic Key-Value Stores
Partial quorum systems are widely used in distributed key-value stores due to their latency benefits at the expense of providing weaker consistency guarantees.
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.
List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier
We further propose folded Lagrange coded computing (FLCC) to incorporate the developed techniques into a specific coded computing 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.
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework.
ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems
While this learning-based approach is more resource-efficient than replication, it is tailored to the specific model hosted by the cloud and is particularly suitable for a small number of queries (typically less than four) and tolerating very few (mostly one) number of stragglers.
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.
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.
All Rivers Run to the Sea: Private Learning with Asymmetric Flows
The main part flows into a small model while the residuals are offloaded to a large model.
