When data is distributed across multiple devices, communication cost often becomes a bottleneck of modern machine learning tasks [38]. This is even more so in federated learning type settings, where communication occurs over bandwidth-limited wireless links [31].

Empirically, S-TEC restructures meaningfully the within-and between-class representations.

Each participating individual should be able tocontribute without the fearofleaking one'ssensitiveinformation. At the same time, thesystem should berobustinthepresence ofmalicious participants inserting corrupted data. Recent algorithmic advances in learning from shared data focus on either one of these threats, leaving the system vulnerable to the other.

Throughout our lives, we learn a huge number of associations between concepts: the taste of a particularfood,themeaningofagesture,ortostopwhenweseearedlight.

In this section, we provide the class assignment of all datasets under different missing rates. The proposed setting is anew multi-task learning scenario. Its practical applications could not be limited by the mentioned assumption in the testing space. Table B.2: The observed classes of each task onOffice-Caltech with different missing rates. Office-Home [9] contains images from four domains/tasks: Artistic, Clipart, Product and Realworld. Skin-Lesion contains three skin lesion classification tasks: HAM10000 [8], Dermofit [2] and Derm7pt[5].

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously.

Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands?

What spatial frequency information do humans and neural networks use to recognize objects?