AffectFAL: Federated Active Affective Computing with Non-IID Data

Federated affective computing, which deploys traditional affective computing in a distributed framework, achieves a trade-off between privacy and utility, and offers a wide variety of applications in business and society. However, the expensive annotation cost of obtaining reliable emotion labels at the local client remains a barrier to the effective use of local emotional data. Therefore, we propose a federated active affective paradigm to improve the performance of federated affective computing with a limited annotation budget on the client. A major challenge in federated active learning is the inconsistency between the active sampling goals of global and local models, particularly in scenarios with Non-IID data across clients, which exacerbates the problem. To address the above challenge, we propose AffectFAL, a federated active affective computing framework. It incorporates a Preference-aware Group Aggregation module, which obtains global models representing the different emotional preferences among clients. We also devise a tailored De-biased Federated Active Sampling strategy with an improved vote entropy, facilitating class balancing of labeled samples and alleviating the problem of sampling goals inconsistency between the global and local models. We evaluate AffectFAL on diverse benchmarks (image, video and physiological signal) and experimental settings for affective computing. Thorough comparisons with other active sampling strategies demonstrate our method's advantages in affective computing for Non-IID federated learning.