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Found 6 papers, 1 papers with code
Sharp Bounds for Sequential Federated Learning on Heterogeneous Data
There are two paradigms in Federated Learning (FL): parallel FL (PFL), where models are trained in a parallel manner across clients; and sequential FL (SFL), where models are trained in a sequential manner across clients.
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size
Adversarial examples are one critical security threat to various visual applications, where injected human-imperceptible perturbations can confuse the output. Generating transferable adversarial examples in the black-box setting is crucial but challenging in practice.
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) sequential FL (SFL), where clients train models in a sequential manner.
Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
To this end, we present SemBLK, a novel method that can learn to generate destructive physical layer semantic attacks for an ESC system under the black-box setting, where the adversaries are imperceptible to humans.
Convergence Analysis of Sequential Split Learning on Heterogeneous Data
In this paper, we derive the convergence guarantees of Sequential SL (SSL, the vanilla case of SL that conducts the model training in sequence) for strongly/general/non-convex objectives on heterogeneous data.
Similarity-based Label Inference Attack against Training and Inference of Split Learning
We mathematically analyze the potential label leakages and propose the cosine and Euclidean similarity measurements for gradients and smashed data, respectively.
