5 accepted papers
Hierarchical Federated Learning (HFL) addresses critical scalability limitations in conventional federated learning by incorporating intermediate aggregation layers, yet optimal topology selection across varying data heterogeneity conditions and network conditions remains an open challenge. This pap…
We aim to provide a unified convergence analysis for permutation-based Stochastic Gradient Descent (SGD), where data examples are permuted before each epoch. By examining the relations among permutations, we categorize existing permutation-based SGD algorithms into three categories: Arbitrary Permut…
Adversarial examples are one critical security threat to various visual applications, where injected human-imperceptible perturbations confuse the output. Generating transferable adversarial examples in the black-box setting is crucial but challenging in practice. Existing input-diversity-based meth…
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. In contrast to that of PFL, the convergenc…
Split learning is a promising paradigm for distributed learning at resource-constrained devices, where the learning model is split to be trained at the participants collaboratively. Unlike federated learning that shares the entire gradients among participants, split learning only exchanges the inter…