Nudges are subtle interventions designed to influence user behavior without restricting choice. Responsible gambling messages (RGMs) exemplify such nudges by encouraging safer decision-making in gambling environments. Prior research has examined how pop-up messages influence gambling behavior in experimental settings and has explored the design of effective slogan messages. However, little is known about how different types of RGMs shape users’ real-world gambling behavior and safety. To address this gap, we apply a nudging perspective to examine how RGMs support gambling safety throughout gamblers’ decision-making journey. We conducted semi-structured interviews with 22 gamblers and found that participants were generally aware of RGMs, yet some misunderstood their intended purpose. Participants perceived the safety impact of RGMs as reflected in both attitudinal and behavioral dimensions. We further discuss users’ message reception practices and the effectiveness of RGMs as nudges, and conclude with design implications for promoting gambling safety.

Personalized federated learning (PFL) has been proposed to overcome the challenge of statistical diversity in clients’ local data distribution under federated scenarios. Some existing PFL methods encourage collaboration between clients to improve overall accuracy. However, these methods often fail to consider the individual preferences of clients; instead, they adopt a top-down decision-making approach to divide clients into disjoint groups. In contrast, we consider the rationality of self-interested clients and allow them to make their own decisions regarding which federation to join. In our proposed PFL framework pFedMGC, we formulate the autonomous decision-making process of clients where they can participate in multiple federations. We then develop a heuristic algorithm to find feasible collaboration in this process. Clients in the same federations collaborate to train a federation-wide global model. To customize these federation models into clients’ personalized models, we employ an adaptive approach to calculate the aggregation weights of federation models based on differences in loss. The experiment results show that pFedMGC can improve overall personalized accuracy up to 28.89%, compared with the state-of-the-art PFL methods.