Portrait de Nicola Neophytou

Nicola Neophytou

Alumni
Google Scholar

Publications

Privacy-Preserving Group Fairness in Cross-Device Federated Learning
Sikha Pentyala
Nicola Neophytou
Anderson Nascimento
Martine De Cock
Golnoosh Farnadi
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are notbiased towards a certain group of peop… (voir plus)le defined by a sensitive attribute such as gender or ethnicity. Achievinggroup fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires usingthe sensitive attribute values of all clients, while FL is aimed precisely at protecting privacy by not givingaccess to the clients’ data. As we show in this paper, this conflict between fairness and privacy in FL can beresolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP). Tothis end, we propose a privacy-preserving approach to calculate group fairness notions in the cross-device FLsetting. Then, we propose two bias mitigation pre-processing and post-processing techniques in cross-deviceFL under formal privacy guarantees, without requiring the clients to disclose their sensitive attribute values.Empirical evaluations on real world datasets demonstrate the effectiveness of our solution to train fair andaccurate ML models in federated cross-device setups with privacy guarantees to the users.
2025-04-23
Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)
proceedings.mlr.press
Embedding Cultural Diversity in Prototype-based Recommender Systems
Armin Moradi
Nicola Neophytou
Florian Carichon
Golnoosh Farnadi
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing u… (voir plus)nderrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
2025-01-01
European Conference on Information Retrieval (publié)
doi.org
arxiv.org
Embedding Cultural Diversity in Prototype-based Recommender Systems
Armin Moradi
Nicola Neophytou
Florian Carichon
Golnoosh Farnadi
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing u… (voir plus)nderrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
2024-12-18
ArXiv (prépublication)
arxiv.org
arxiv.org
Embedding Cultural Diversity in Prototype-based Recommender Systems
Armin Moradi
Nicola Neophytou
Florian Carichon
Golnoosh Farnadi
Popularity bias in recommender systems can increase cultural overrepresentation by favoring norms from dominant cultures and marginalizing u… (voir plus)nderrepresented groups. This issue is critical for platforms offering cultural products, as they influence consumption patterns and human perceptions. In this work, we address popularity bias by identifying demographic biases within prototype-based matrix factorization methods. Using the country of origin as a proxy for cultural identity, we link this demographic attribute to popularity bias by refining the embedding space learning process. First, we propose filtering out irrelevant prototypes to improve representativity. Second, we introduce a regularization technique to enforce a uniform distribution of prototypes within the embedding space. Across four datasets, our results demonstrate a 27\% reduction in the average rank of long-tail items and a 2\% reduction in the average rank of items from underrepresented countries. Additionally, our model achieves a 2\% improvement in HitRatio@10 compared to the state-of-the-art, highlighting that fairness is enhanced without compromising recommendation quality. Moreover, the distribution of prototypes leads to more inclusive explanations by better aligning items with diverse prototypes.
2024-12-18
ArXiv (prépublication)
doi.org
arxiv.org
Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations
Armin Moradi
Nicola Neophytou
Golnoosh Farnadi
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
Promoting Fair Vaccination Strategies through Influence Maximization: A Case Study on COVID-19 Spread
Nicola Neophytou
Afaf Taïk
Golnoosh Farnadi
The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such dispa… (voir plus)rities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obligations. Moreover, senior citizens were found to be more susceptible to severe symptoms, largely due to age-related health reasons. Adapting vaccine distribution strategies to consider a range of demographics is therefore essential to address these disparities. In this study, we propose a novel approach that utilizes influence maximization (IM) on mobility networks to develop vaccination strategies which incorporate demographic fairness. By considering factors such as race, social status, age, and associated risk factors, we aim to optimize vaccine distribution to achieve various fairness definitions for one or more protected attributes at a time. Through extensive experiments conducted on Covid-19 spread in three major metropolitan areas across the United States, we demonstrate the effectiveness of our proposed approach in reducing disease transmission and promoting fairness in vaccination distribution.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org