
Ph.D. candidate in Ingegneria Informatica E Dei Sistemi , 37th cycle (2021-2024)
Department of Control and Computer Engineering (DAUIN)
Profile
PhD
Thesis title
Addressing Heterogeneity in Federated Learning for Real-world Vision Applications
Research topic
Addressing Heterogeneity in Federated Learning for Real-world Vision Applications
Tutors
Research presentation
Research interests
Biography
Awards and Honors
Teaching
Teachings
Master of Science
- Applicazioni Web I. A.A. 2022/23, INGEGNERIA INFORMATICA (COMPUTER ENGINEERING). Collaboratore del corso
Bachelor of Science
- Informatica. A.A. 2022/23, INGEGNERIA GESTIONALE. Collaboratore del corso
Publications
Latest publications View all publications in Porto@Iris
- Fani, Eros; Camoriano, Raffaello; Caputo, Barbara; Ciccone, Marco (In stampa)
Resource-Efficient Personalization in Federated Learning with Closed-Form Classifiers. In: IEEE ACCESS. ISSN 2169-3536
Contributo su Rivista - Fanì, Eros; Camoriano, Raffaello; Caputo, Barbara; Ciccone, Marco (2024)
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers. In: Forty-first International Conference on Machine Learning (ICML), Wien, Austria, July 21 - July 27, 2024, pp. 13029-13048. ISSN 2640-3498
Contributo in Atti di Convegno (Proceeding) - Dutto, Mattia; Berton, Gabriele; Caldarola, Debora; Fani, Eros; Trivigno, Gabriele; ... (2024)
Collaborative Visual Place Recognition through Federated Learning. In: IEEE / CVF Computer Vision and Pattern Recognition Conference Workshop, Seattle (USA), 17-18 June 2024, pp. 4215-4225. ISBN: 979-8-3503-6547-4
Contributo in Atti di Convegno (Proceeding) - Fani', Eros; Ciccone, Marco; Caputo, Barbara (2023)
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving. In: 2023 I-RIM Conference, Roma (ITA), 20-22 ottobre 2023, pp. 81-84. ISBN: 9788894580549
Contributo in Atti di Convegno (Proceeding) - Fani', Eros; Camoriano, Raffaello; Caputo, Barbara; Ciccone, Marco (2023)
Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models. In: Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023, New Orleans, LA (USA), Dec 16 2023, pp. 1-19
Contributo in Atti di Convegno (Proceeding)