Computer and Information Technology Seminar

Helping Older Users with their Smartphone: The Role of Social Support

Older adults often experience a range of challenges when using smartphones, adding to a growing digital divide between generations and limiting access to a wide range of socio-economic interactions (e.g., healthcare, socializing). One major way to overcome these challenges is social support, that is, assistance from family, friends, and other social peers through interpersonal communication. The goal of the present study is to provide insights into how social support can help older users in overcoming their challenges with smartphone usage. To this end, we conducted an online survey with social peers who have helped older users in their network with their smartphone usage. Based on cluster analysis, we identify three main helper communication types, each of which perceive and approach their interactions with older smartphone users in a different way. Our findings provide important insights into how social peers can help older users with developing understanding and building independence.

BadFU: Backdoor Federated Learning through Adversarial Machine Unlearning

Federated learning (FL) has been widely adopted as a decentralized training paradigm that enables multiple clients to collaboratively learn a shared model without exposing their local data. As concerns over data privacy and regulatory compliance grow, machine unlearning, which aims to remove the influence of specific data from trained models, has become increasingly important in the federated setting to meet legal, ethical, or user-driven demands. However, integrating unlearning into FL introduces new challenges and raises largely unexplored security risks. In particular, adversaries may exploit the unlearning process to compromise the integrity of the global model. In this paper, we present the first backdoor attack in the context of federated unlearning, demonstrating that an adversary can inject backdoors into the global model through seemingly legitimate unlearning requests. Specifically, we propose BadFU, an attack strategy where a malicious client uses both backdoor and camouflage samples to train the global model normally during the federated training process. Once the client requests unlearning of the camouflage samples, the global model transitions into a backdoored state. Extensive experiments under various FL frameworks and unlearning strategies validate the effectiveness of BadFU, revealing a critical vulnerability in current federated unlearning practices and underscoring the urgent need for more secure and robust federated unlearning mechanisms.

From Principles to Practice: A Deep Dive into AI Ethics and Regulations

This talk thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union. It delves into the fundamental ethical principles of safety, transparency, non-discrimination, traceability, and environmental sustainability for AI developments and deployments. Considering the technical efforts and strategies undertaken by academics and industry to uphold these principles, we explore the synergies and conflicts among the five ethical principles. Through this lens, work presents a forward-looking perspective on the future of AI regulations, advocating for a harmonized approach that safeguards societal values while encouraging technological advancement.

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