TY - JOUR

T1 - FAC-fed

T2 - Federated adaptation for fairness and concept drift aware stream classification

AU - Badar, Maryam

AU - Nejdl, Wolfgang

AU - Fisichella, Marco

N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. The Lower Saxony Ministry of Science and Culture (Niedersächsische Ministerium für Wissenschaft und Kultur). This research was partially funded by the Lower Saxony Ministry of Science and Culture, Germany (Niedersächsische Ministerium für Wissenschaft und Kultur) and the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor with grant No. 01DD20003.

PY - 2023/8

Y1 - 2023/8

N2 - Federated learning is an emerging collaborative learning paradigm of Machine learning involving distributed and heterogeneous clients. Enormous collections of continuously arriving heterogeneous data residing on distributed clients require federated adaptation of efficient mining algorithms to enable fair and high-quality predictions with privacy guarantees and minimal response delay. In this context, we propose a federated adaptation that mitigates discrimination embedded in the streaming data while handling concept drifts (FAC-Fed). We present a novel adaptive data augmentation method that mitigates client-side discrimination embedded in the data during optimization, resulting in an optimized and fair centralized server. Extensive experiments on a set of publicly available streaming and static datasets confirm the effectiveness of the proposed method. To the best of our knowledge, this work is the first attempt towards fairness-aware federated adaptation for stream classification, therefore, to prove the superiority of our proposed method over state-of-the-art, we compare the centralized version of our proposed method with three centralized stream classification baseline models (FABBOO, FAHT, CSMOTE). The experimental results show that our method outperforms the current methods in terms of both discrimination mitigation and predictive performance.

AB - Federated learning is an emerging collaborative learning paradigm of Machine learning involving distributed and heterogeneous clients. Enormous collections of continuously arriving heterogeneous data residing on distributed clients require federated adaptation of efficient mining algorithms to enable fair and high-quality predictions with privacy guarantees and minimal response delay. In this context, we propose a federated adaptation that mitigates discrimination embedded in the streaming data while handling concept drifts (FAC-Fed). We present a novel adaptive data augmentation method that mitigates client-side discrimination embedded in the data during optimization, resulting in an optimized and fair centralized server. Extensive experiments on a set of publicly available streaming and static datasets confirm the effectiveness of the proposed method. To the best of our knowledge, this work is the first attempt towards fairness-aware federated adaptation for stream classification, therefore, to prove the superiority of our proposed method over state-of-the-art, we compare the centralized version of our proposed method with three centralized stream classification baseline models (FABBOO, FAHT, CSMOTE). The experimental results show that our method outperforms the current methods in terms of both discrimination mitigation and predictive performance.

KW - Deep neural network

KW - Fairness

KW - Federated learning

KW - Hedge backpropagation

KW - Privacy

KW - Stream classification

UR - http://www.scopus.com/inward/record.url?scp=85165872586&partnerID=8YFLogxK

U2 - 10.1007/s10994-023-06360-7

DO - 10.1007/s10994-023-06360-7

M3 - Article

AN - SCOPUS:85165872586

VL - 112

SP - 2761

EP - 2786

JO - Machine learning

JF - Machine learning

SN - 0885-6125

IS - 8

ER -