Per Year

@article{ndnfl-fgcs2023,
   author = {Agiollo, Andrea and Enkeleda Bardhi and Mauro Conti and Nicolò Dal Fabbro and Riccardo Lazzeretti},
   doi = {10.1016/j.future.2023.11.009},
   issn = {0167-739X},
   journal = {Future Generation Computer Systems},
   keywords = {Anonymous communication, Federated Learning, Named Data Networking, Privacy-preserving},
   pages = {288--303},
   publisher = {Elsevier},
   note = {Special Issue on Federated Learning on the Edge: Challenges and Future Directions},
   title = {Anonymous Federated Learning via Named-Data Networking},
   url = {https://www.sciencedirect.com/science/article/pii/S0167739X23004144},
   volume = 152,
   year = 2024
}

@inproceedings{samis-kdd2024,
   acm = {3671985},
   author = {Agiollo, Andrea and Young In Kim and Khanna, Rajiv},
   dblp = {conf/kdd/AgiolloKK24},
   doi = {10.1145/3637528.3671985},
   journal = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24), August 25–29, 2024, Barcelona, Spain},
   keywords = {Neural Networks, Data-efficient Learning, Memorization, Flatness},
   month = aug,
   numpages = 12,
   pages = {17--28},
   publisher = {ACM},
   scholar = {8151925308990231563},
   title = {Approximating Memorization Using Loss Surface Geometry for Dataset Pruning and Summarization},
   url = {https://dl.acm.org/doi/10.1145/3637528.3671985},
   urlopenaccess = {https://dl.acm.org/doi/pdf/10.1145/3637528.3671985},
   urlpdf = {https://dl.acm.org/doi/pdf/10.1145/3637528.3671985},
   year = 2024
}