  Computational understanding of humor is an important topic under creative
language understanding and modeling. It can play a key role in complex human-AI
interactions. The challenge here is that human perception of humorous content
is highly subjective. The same joke may receive different funniness ratings
from different readers. This makes it highly challenging for humor recognition
models to achieve personalization in practical scenarios. Existing approaches
are generally designed based on the assumption that users have a consensus on
whether a given text is humorous or not. Thus, they cannot handle diverse humor
preferences well. In this paper, we propose the FedHumor approach for the
recognition of humorous content in a personalized manner through Federated
Learning (FL). Extending a pre-trained language model, FedHumor guides the
fine-tuning process by considering diverse distributions of humor preferences
from individuals. It incorporates a diversity adaptation strategy into the FL
paradigm to train a personalized humor recognition model. To the best of our
knowledge, FedHumor is the first text-based personalized humor recognition
model through federated learning. Extensive experiments demonstrate the
advantage of FedHumor in recognizing humorous texts compared to nine
state-of-the-art humor recognition approaches with superior capability for
handling the diversity in humor labels produced by users with diverse
preferences.
