{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T20:19:22Z","timestamp":1758399562894,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,20]],"date-time":"2021-11-20T00:00:00Z","timestamp":1637366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100009880","name":"Regione Lazio","doi-asserted-by":"publisher","award":["A0375-2020-36491"],"award-info":[{"award-number":["A0375-2020-36491"]}],"id":[{"id":"10.13039\/501100009880","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,20]]},"DOI":"10.1145\/3505711.3505717","type":"proceedings-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T02:20:17Z","timestamp":1648520417000},"page":"38-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["AdaFed: Performance-based Adaptive Federated Learning"],"prefix":"10.1145","author":[{"given":"Alessandro","family":"Giuseppi","sequence":"first","affiliation":[{"name":"DiAG, Sapienza,University of Rome, Italy"}]},{"given":"Lucrezia","family":"Della Torre","sequence":"additional","affiliation":[{"name":"DiAG, Sapienza,University of Rome, Italy"}]},{"given":"Danilo","family":"Menegatti","sequence":"additional","affiliation":[{"name":"DiAG, Sapienza,University of Rome, Italy"}]},{"given":"Antonio","family":"Pietrabissa","sequence":"additional","affiliation":[{"name":"DiAG, Sapienza,University of Rome, Italy"}]}],"member":"320","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Seyed\u00a0Mehdi Ayyoubzadeh and Xiaolin Wu. 2020. Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques. arXiv:2001.07766  Seyed\u00a0Mehdi Ayyoubzadeh and Xiaolin Wu. 2020. Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques. arXiv:2001.07766"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00446"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"e_1_3_2_1_5_1","volume-title":"FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare","author":"Chen Yiqiang","year":"2020","unstructured":"Yiqiang Chen , Xin Qin , Jindong Wang , Chaohui Yu , and Wen Gao . 2020. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare . IEEE Intelligent Systems( 2020 ), 1\u20131. https:\/\/doi.org\/10.1109\/mis.2020.2988604 Yiqiang Chen, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao. 2020. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. IEEE Intelligent Systems(2020), 1\u20131. https:\/\/doi.org\/10.1109\/mis.2020.2988604"},{"key":"e_1_3_2_1_6_1","volume-title":"Focus: Dealing with label quality disparity in federated learning. arXiv:2001.11359","author":"Chen Yiqiang","year":"2020","unstructured":"Yiqiang Chen , Xiaodong Yang , Xin Qin , Han Yu , Biao Chen , and Zhiqi Shen . 2020 . Focus: Dealing with label quality disparity in federated learning. arXiv:2001.11359 Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, and Zhiqi Shen. 2020. Focus: Dealing with label quality disparity in federated learning. arXiv:2001.11359"},{"key":"e_1_3_2_1_7_1","volume-title":"Class-Balanced Loss Based on Effective Number of Samples. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https:\/\/doi.org\/10","author":"Cui Yin","year":"2019","unstructured":"Yin Cui , Menglin Jia , Tsung-Yi Lin , Yang Song , and Serge Belongie . 2019 . Class-Balanced Loss Based on Effective Number of Samples. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https:\/\/doi.org\/10 .1109\/cvpr.2019.00949 Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. 2019. Class-Balanced Loss Based on Effective Number of Samples. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https:\/\/doi.org\/10.1109\/cvpr.2019.00949"},{"key":"e_1_3_2_1_8_1","series-title":"Lecture Notes in Computer Science","volume-title":"A desicion-theoretic generalization of on-line learning and an application to boosting","author":"Freund Yoav","unstructured":"Yoav Freund and Robert\u00a0 E. Schapire . 1995. A desicion-theoretic generalization of on-line learning and an application to boosting . In Lecture Notes in Computer Science . Springer Berlin Heidelberg , 23\u201337. https:\/\/doi.org\/10.1007\/3-540-59119-2_166 Yoav Freund and Robert\u00a0E. Schapire. 1995. A desicion-theoretic generalization of on-line learning and an application to boosting. In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 23\u201337. https:\/\/doi.org\/10.1007\/3-540-59119-2_166"},{"key":"e_1_3_2_1_9_1","unstructured":"Robin\u00a0C. Geyer Tassilo Klein and Moin Nabi. 2017. Differentially Private Federated Learning: A Client Level Perspective. arXiv:1712.07557  Robin\u00a0C. Geyer Tassilo Klein and Moin Nabi. 2017. Differentially Private Federated Learning: A Client Level Perspective. arXiv:1712.07557"},{"key":"e_1_3_2_1_10_1","unstructured":"Andrew Hard Kanishka Rao Rajiv Mathews Swaroop Ramaswamy Fran\u00e7oise Beaufays Sean Augenstein Hubert Eichner Chlo\u00e9 Kiddon and Daniel Ramage. 2018. Federated Learning for Mobile Keyboard Prediction. arXiv:1811.03604  Andrew Hard Kanishka Rao Rajiv Mathews Swaroop Ramaswamy Fran\u00e7oise Beaufays Sean Augenstein Hubert Eichner Chlo\u00e9 Kiddon and Daniel Ramage. 2018. Federated Learning for Mobile Keyboard Prediction. arXiv:1811.03604"},{"key":"e_1_3_2_1_11_1","unstructured":"A.\u00a0Ali Heydari Craig\u00a0A. Thompson and Asif Mehmood. 2019. SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions. arXiv:1912.12355  A.\u00a0Ali Heydari Craig\u00a0A. Thompson and Asif Mehmood. 2019. SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions. arXiv:1912.12355"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI49370.2019.00218"},{"key":"e_1_3_2_1_13_1","volume-title":"Federated Learning: Strategies for Improving Communication Efficiency. arXiv:1610.05492","author":"Kone\u010dn\u00fd Jakub","year":"2016","unstructured":"Jakub Kone\u010dn\u00fd , H.\u00a0 Brendan McMahan , Felix\u00a0 X. Yu , Peter Richt\u00e1rik , Ananda\u00a0Theertha Suresh , and Dave Bacon . 2016 . Federated Learning: Strategies for Improving Communication Efficiency. arXiv:1610.05492 Jakub Kone\u010dn\u00fd, H.\u00a0Brendan McMahan, Felix\u00a0X. Yu, Peter Richt\u00e1rik, Ananda\u00a0Theertha Suresh, and Dave Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. arXiv:1610.05492"},{"key":"e_1_3_2_1_14_1","unstructured":"Yann LeCun Corinna Cortes and CJ Burges. 2010. MNIST handwritten digit database. ATT Labs [Online]. Available: http:\/\/yann.lecun.com\/exdb\/mnist 2(2010).  Yann LeCun Corinna Cortes and CJ Burges. 2010. MNIST handwritten digit database. ATT Labs [Online]. Available: http:\/\/yann.lecun.com\/exdb\/mnist 2(2010)."},{"key":"e_1_3_2_1_15_1","unstructured":"Qinbin Li Zeyi Wen Zhaomin Wu Sixu Hu Naibo Wang and Bingsheng He. 2019. A Survey on Federated Learning Systems: Vision Hype and Reality for Data Privacy and Protection. arXiv:1907.09693  Qinbin Li Zeyi Wen Zhaomin Wu Sixu Hu Naibo Wang and Bingsheng He. 2019. A Survey on Federated Learning Systems: Vision Hype and Reality for Data Privacy and Protection. arXiv:1907.09693"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"e_1_3_2_1_17_1","unstructured":"Tian Li Anit\u00a0Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. arxiv:1812.06127\u00a0[cs.LG]  Tian Li Anit\u00a0Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. arxiv:1812.06127\u00a0[cs.LG]"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8"},{"key":"e_1_3_2_1_19_1","volume-title":"Focal Loss for Dense Object Detection. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE. https:\/\/doi.org\/10","author":"Lin Tsung-Yi","year":"2017","unstructured":"Tsung-Yi Lin , Priya Goyal , Ross Girshick , Kaiming He , and Piotr Dollar . 2017 . Focal Loss for Dense Object Detection. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE. https:\/\/doi.org\/10 .1109\/iccv.2017.324 Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. 2017. Focal Loss for Dense Object Detection. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE. https:\/\/doi.org\/10.1109\/iccv.2017.324"},{"key":"e_1_3_2_1_20_1","unstructured":"Yujun Lin Song Han Huizi Mao Yu Wang and William\u00a0J. Dally. 2017. Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. arXiv:1712.01887  Yujun Lin Song Han Huizi Mao Yu Wang and William\u00a0J. Dally. 2017. Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. arXiv:1712.01887"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Yang Liu Anbu Huang Yun Luo He Huang Youzhi Liu Yuanyuan Chen Lican Feng Tianjian Chen Han Yu and Qiang Yang. 2020. FedVision: An Online Visual Object Detection Platform Powered by Federated Learning. arXiv:2001.06202  Yang Liu Anbu Huang Yun Luo He Huang Youzhi Liu Yuanyuan Chen Lican Feng Tianjian Chen Han Yu and Qiang Yang. 2020. FedVision: An Online Visual Object Detection Platform Powered by Federated Learning. arXiv:2001.06202","DOI":"10.1609\/aaai.v34i08.7021"},{"key":"e_1_3_2_1_22_1","unstructured":"Jiahuan Luo Xueyang Wu Yun Luo Anbu Huang Yunfeng Huang Yang Liu and Qiang Yang. 2019. Real-World Image Datasets for Federated Learning. arXiv:1910.11089  Jiahuan Luo Xueyang Wu Yun Luo Anbu Huang Yunfeng Huang Yang Liu and Qiang Yang. 2019. Real-World Image Datasets for Federated Learning. arXiv:1910.11089"},{"key":"e_1_3_2_1_23_1","volume-title":"Proceedings of the 20 th International Conference on Artificial Intelligence and Statistics (AISTATS)","volume":"54","author":"McMahan Brendan","year":"2016","unstructured":"H.\u00a0 Brendan McMahan , Eider Moore , Daniel Ramage , and Seth Hampson . 2016 . Communication-Efficient Learning of Deep Networks from Decentralized Data . In Proceedings of the 20 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017. JMLR: W&CP volume 54 . arXiv:1602.05629 H.\u00a0Brendan McMahan, Eider Moore, Daniel Ramage, and Seth Hampson. 2016. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017. JMLR: W&CP volume 54. arXiv:1602.05629"},{"key":"e_1_3_2_1_24_1","unstructured":"H.\u00a0Brendan McMahan Eider Moore Daniel Ramage and Blaise\u00a0Ag\u00fcera y Arcas. 2016. Federated Learning of Deep Networks using Model Averaging. arXiv:1602.05629  H.\u00a0Brendan McMahan Eider Moore Daniel Ramage and Blaise\u00a0Ag\u00fcera y Arcas. 2016. Federated Learning of Deep Networks using Model Averaging. arXiv:1602.05629"},{"key":"e_1_3_2_1_25_1","unstructured":"Conrado\u00a0Silva Miranda and Fernando Jos\u00e9\u00a0Von Zuben. 2015. Multi-Objective Optimization for Self-Adjusting Weighted Gradient in Machine Learning Tasks. arXiv:1506.01113  Conrado\u00a0Silva Miranda and Fernando Jos\u00e9\u00a0Von Zuben. 2015. Multi-Objective Optimization for Self-Adjusting Weighted Gradient in Machine Learning Tasks. arXiv:1506.01113"},{"key":"e_1_3_2_1_26_1","unstructured":"Milad Nasr Reza Shokri and Amir Houmansadr. 2018. Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks. arXiv:1812.00910  Milad Nasr Reza Shokri and Amir Houmansadr. 2018. Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks. arXiv:1812.00910"},{"key":"e_1_3_2_1_27_1","unstructured":"Official Keras.io documentation. [n.d.]. Example of Convolutional Neural Network for MNIST. https:\/\/keras.io\/examples\/mnist_cnn\/  Official Keras.io documentation. [n.d.]. Example of Convolutional Neural Network for MNIST. https:\/\/keras.io\/examples\/mnist_cnn\/"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"volume-title":"The Shapley value: essays in honor of Lloyd S. Shapley","author":"Roth E","key":"e_1_3_2_1_29_1","unstructured":"Alvin\u00a0 E Roth . 1988. The Shapley value: essays in honor of Lloyd S. Shapley . Cambridge University Press . Alvin\u00a0E Roth. 1988. The Shapley value: essays in honor of Lloyd S. Shapley. Cambridge University Press."},{"key":"e_1_3_2_1_30_1","volume-title":"Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler , Simon Wiedemann , Klaus-Robert Muller , and Wojciech Samek . 2019. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data . IEEE Transactions on Neural Networks and Learning Systems ( 2019 ), 1\u201314. https:\/\/doi.org\/10.1109\/tnnls.2019.2944481 Felix Sattler, Simon Wiedemann, Klaus-Robert Muller, and Wojciech Samek. 2019. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data. IEEE Transactions on Neural Networks and Learning Systems (2019), 1\u201314. https:\/\/doi.org\/10.1109\/tnnls.2019.2944481"},{"volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Sheller J.","key":"e_1_3_2_1_31_1","unstructured":"Micah\u00a0 J. Sheller , G.\u00a0 Anthony Reina , Brandon Edwards , Jason Martin , and Spyridon Bakas . 2019. Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation . In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries . Springer International Publishing , 92\u2013104. https:\/\/doi.org\/10.1007\/978-3-030-11723-8_9 Micah\u00a0J. Sheller, G.\u00a0Anthony Reina, Brandon Edwards, Jason Martin, and Spyridon Bakas. 2019. Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 92\u2013104. https:\/\/doi.org\/10.1007\/978-3-030-11723-8_9"},{"key":"e_1_3_2_1_32_1","volume-title":"Profit Allocation for Federated Learning. In 2019 IEEE International Conference on Big Data (Big Data). IEEE. https:\/\/doi.org\/10","author":"Song Tianshu","year":"2019","unstructured":"Tianshu Song , Yongxin Tong , and Shuyue Wei . 2019 . Profit Allocation for Federated Learning. In 2019 IEEE International Conference on Big Data (Big Data). IEEE. https:\/\/doi.org\/10 .1109\/bigdata47090.2019.9006327 Tianshu Song, Yongxin Tong, and Shuyue Wei. 2019. Profit Allocation for Federated Learning. In 2019 IEEE International Conference on Big Data (Big Data). IEEE. https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006327"},{"key":"e_1_3_2_1_33_1","volume-title":"Adaloss: Adaptive Loss Function for Landmark Localization. arXiv:1908.01070","author":"Teixeira Brian","year":"2019","unstructured":"Brian Teixeira , Birgi Tamersoy , Vivek Singh , and Ankur Kapoor . 2019 . Adaloss: Adaptive Loss Function for Landmark Localization. arXiv:1908.01070 Brian Teixeira, Birgi Tamersoy, Vivek Singh, and Ankur Kapoor. 2019. Adaloss: Adaptive Loss Function for Landmark Localization. arXiv:1908.01070"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357370"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Tianhao Wang Johannes Rausch Ce Zhang Ruoxi Jia and Dawn Song. 2020. A Principled Approach to Data Valuation for Federated Learning. arxiv:2009.06192\u00a0[cs.LG]  Tianhao Wang Johannes Rausch Ce Zhang Ruoxi Jia and Dawn Song. 2020. A Principled Approach to Data Valuation for Federated Learning. arxiv:2009.06192\u00a0[cs.LG]","DOI":"10.1007\/978-3-030-63076-8_11"},{"key":"e_1_3_2_1_36_1","volume-title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE. https:\/\/doi.org\/10","author":"Wang Zhibo","year":"2019","unstructured":"Zhibo Wang , Mengkai Song , Zhifei Zhang , Yang Song , Qian Wang , and Hairong Qi . 2019 . Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE. https:\/\/doi.org\/10 .1109\/infocom.2019.8737416 Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi. 2019. Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE. https:\/\/doi.org\/10.1109\/infocom.2019.8737416"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_1_38_1","unstructured":"Timothy Yang Galen Andrew Hubert Eichner Haicheng Sun Wei Li Nicholas Kong Daniel Ramage and Fran\u00e7oise Beaufays. 2018. Applied Federated Learning: Improving Google Keyboard Query Suggestions. arXiv:1812.02903  Timothy Yang Galen Andrew Hubert Eichner Haicheng Sun Wei Li Nicholas Kong Daniel Ramage and Fran\u00e7oise Beaufays. 2018. Applied Federated Learning: Improving Google Keyboard Query Suggestions. arXiv:1812.02903"}],"event":{"name":"ICAAI 2021: 2021 the 5th International Conference on Advances in Artificial Intelligence","acronym":"ICAAI 2021","location":"Virtual Event United Kingdom"},"container-title":["2021 The 5th International Conference on Advances in Artificial Intelligence (ICAAI)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3505711.3505717","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3505711.3505717","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:49Z","timestamp":1750191109000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3505711.3505717"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,20]]},"references-count":38,"alternative-id":["10.1145\/3505711.3505717","10.1145\/3505711"],"URL":"https:\/\/doi.org\/10.1145\/3505711.3505717","relation":{},"subject":[],"published":{"date-parts":[[2021,11,20]]},"assertion":[{"value":"2022-03-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}