Communication efficiency in federated learning (FL) remains a critical challenge in resource-constrained environments. While prototype-based FL reduces communication overhead by sharing class prototypes—mean activations in the penultimate layer—instead of model parameters, its efficiency degrades with larger feature dimensions and class counts. We propose TinyProto, which addresses these limitations through Class-wise Prototype Sparsification (CPS) and Adaptive Prototype Scaling (APS). CPS enables structured sparsity by allocating specific dimensions to class prototypes and transmitting only non-zero elements, thereby achieving higher communication efficiency, while APS scales prototypes based on class distributions to improve performance. Our experiments demonstrate that TinyProto reduces communication costs by up to 10x compared to existing methods while improving performance. Beyond communication efficiency, TinyProto offers crucial advantages: it achieves compression without client-side computational overhead and supports heterogeneous architectures, making it particularly suitable for resource-constrained heterogeneous FL scenarios.

In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable knowledge from human-object interactions (HOI). While WSOD shows lower performance than full supervision, we mainly focus on HOI as the main context which can strongly supervise complex semantics in images. Therefore, we propose a novel module called RRPN (relational region proposal network) which outputs an object-localizing attention map only with human poses and action verbs. In the source domain, we fully train an object detector and the RRPN with full supervision of HOI. With transferred knowledge about localization map from the trained RRPN, a new object detector can learn unseen objects with weak verbal supervision of HOI without bounding box annotations in the target domain. Because the RRPN is designed as an add-on type, we can apply it not only to the object detection but also to other domains such as semantic segmentation. The experimental results on HICO-DET dataset show the possibility that the proposed method can be a cheap alternative for the current supervised object detection paradigm. Moreover, qualitative results demonstrate that our model can properly localize unseen objects on HICO-DET and V-COCO datasets.

We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way. Such learning approach is risky when the interacting environment includes an expanse of state space because it is then almost impossible to foresee all unwanted outcomes and penalize them with negative rewards beforehand. We propose Action-conditional $\beta$-VAE (AC-$\beta$-VAE) that allows succinct mappings of action-dependent factors in desirable dimensions of latent representations while disentangling environmental factors. Our proposed method tackles the blackbox issue by encouraging an RL policy network to learn interpretable latent features by distinguits influenshing ices from uncontrollable environmental factors, which closely resembles the way humans understand their scenes. Our experimental results show that the learned latent factors not only are interpretable, but also enable modeling the distribution of entire visited state-action space. We have experimented that this characteristic of the proposed structure can lead to ex post facto governance for desired behaviors of RL agents.