Semi-Supervised Federated Learning (SSFL) aims to improve a pretrained model using unlabeled data from clients. Traditional SSFL solutions relying on pseudo-labels or autoencoders often struggle in the presence of domain shift, i.e. a difference in data distributions between the server and the clients. In this paper we present SemiFDA, the first solution to effectively handle domain shift in SSFL. After training an initial classifier on the server's labeled data, we establish an unsupervised learning process at clients to train feature extractors based on encoders. This process adopts a custom unsupervised loss function that promotes the clients' encoders to align their feature distributions with those extracted by the encoder at server. The updated encoders are then aggregated at the server using Federated Aver-aging and sent back for the next iteration, while the classification head remains frozen to preserve the benefits of aligning features locally. Furthermore, we design an experimental framework to mimic various levels of domain shift and test SSFL methods in real-world scenarios, including HAR and Digit Classification. Our results also demonstrate the detrimental effects of domain shift in SSFL and show that SemiFDA outperforms other solutions under these challenging conditions.

The kinetostatic analysis of manipulator performances at the end effector (EE) has traditionally been based on the computation of the so-called manipulability ellipsoids rather than on polytopes which are more accurate a representation. A reason why polytopes have received less attention is their being less tractable from a computational viewpoint. This paper presents an effective computation tool based on polytopes, with separation between translational and rotational analyses. This eliminates the ambiguity inherent in the conventional rototranslational formulation. Examples of application of this tool to the kinetostatic performance analysis of a redundant arm are finally provided, with numerical results.

A planning system for recognizing connected letters belonging to the set [E1 = (P,T,K,B,D,V,E,G,C,3) ] is described. Experimental results related to ten (five male and five female) speakers are reported.