In this paper, we provide lower bounds for Differentially Private (DP) Online Learning algorithms. Our result shows that, for a broad class of $(\epsilon,\delta)$-DP online algorithms, for number of rounds $T$ such that $\log T\leq O\left(1 / \delta\right)$, the expected number of mistakes incurred by the algorithm grows as \(\Omega\left(\log T\right)\). This matches the upper bound obtained by Golowich and Livni (2021) and is in contrast to non-private online learning where the number of mistakes is independent of \(T\). To the best of our knowledge, our work is the first result towards settling lower bounds for DP–Online learning and partially addresses the open question in Sanyal and Ramponi (2022).

We consider learning a probabilistic classifier from partially-labelled supervision (inputs denoted with multiple possibilities) using standard neural architectures with a softmax as the final layer. We identify a bias phenomenon that can arise from the softmax layer in even simple architectures that prevents proper exploration of alternative options, making the dynamics of gradient descent overly sensitive to initialization. We introduce a novel loss function that allows for unbiased exploration within the space of alternative outputs. We give a theoretical justification for our loss function, and provide an extensive evaluation of its impact on synthetic data, on standard partially labelled benchmarks and on a contributed novel benchmark related to an existing rule learning challenge.

Arrowhead is an IIoT (Industrial IoT) framework that dynamically and flexibly supports automated manufacturing processes following Industry 4.0 expectations. This technical paper describes how its workflow management system, i.e., the Workflow Choreographer supports automated production. The motivation behind this work is to make production workflow management and execution more flexible by applying the SOA (Service-Oriented Architecture) principles that help information exchange between various, loosely coupled systems through late binding and lookup of their services. This paper further details the functions of the Workflow Choreographer system and shows how it applies the identified workflow levels in practice to provide a solution for the given challenge. To validate its feasibility, the paper presents simple use cases where the Workflow Choreographer manages typical production processes under laboratory conditions.