… Training protocols with N=3 clients for federated learning (FL), split learning (SL) and our
proposed AdaSplit which builds upon split learning framework. AdaSplit improves i) …

… Recent studies have proposed Split Learning (SL) for … We propose Adaptive REsource-aware
Split-learning (ARES)… training through device-targeted split points while accounting for time…

… Abstract—Federated learning (FL) has gained increasing … distributed learning scheme that
integrates FL with a model split … model locally, the model is split and trained among all clients …

… -efficient split learning (SL) … : (i) adaptive feature-wise dropout and (ii) adaptive feature-wise
quantization. In the first strategy, the intermediate feature vectors are dropped with adaptive …

… model splitting (MS) and client-side model aggregation (MA) on the learning performance,
serving as a theoretical foundation. Then, we propose AdaptSFL, a novel resource-adaptive …

… directly learn the original client models. Inspired by the idea of split learning [27, 32] we split
each … For example, as for multimodal integration tasks [3, 20, 25, 37], which learn predictive …

… in a distributed manner, based on a same fixed split point. However, due to the device …
design an adaptive split learning (ASL) scheme which can dynamically select split points for …

… Adaptive split ratio. Now, the question is how much we allocate the partition of each task. In
… Other than the final layer, we propose an adaptive splitting scheme such that |v (l) o | : |v(l) n | …

… In this work, we propose a principled meta-learning based approach to OCDA for semantic
… Meanwhile, we learn to online update the model by modelagnostic meta-learning (MAML) …

… Moreover, implementing the adaptive threshold mechanism results in a reduction in the …
We can see how adding the AE with the adaptive threshold mechanism makes split learning …