{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T08:39:41Z","timestamp":1766219981684,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4500800"],"award-info":[{"award-number":["2022YFB4500800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103325"],"award-info":[{"award-number":["62103325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202216"],"award-info":[{"award-number":["62202216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi High-Level Talent Program","award":["2021QCYRC4-26"],"award-info":[{"award-number":["2021QCYRC4-26"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation Grant","award":["2023A1515010244"],"award-info":[{"award-number":["2023A1515010244"]}]},{"name":"Shenzhen Science and Technology Program","award":["20231121101752002"],"award-info":[{"award-number":["20231121101752002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,9,8]]},"DOI":"10.1145\/3754598.3754602","type":"proceedings-article","created":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T08:34:32Z","timestamp":1766219672000},"page":"248-257","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneity-aware Task Scheduling based on Personalized Federated Reinforcement Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9380-7943","authenticated-orcid":false,"given":"Xin","family":"Yong","sequence":"first","affiliation":[{"name":"Xi'an Jiaotong University, xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-1345","authenticated-orcid":false,"given":"Li","family":"Yan","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1903-6428","authenticated-orcid":false,"given":"Zhuozhao","family":"Li","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2025,12,20]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Jialin Lu Jing Yang Shaobo Li Yijun Li Wu Jiang Jiangtian Dai and Jianjun Hu. 2024. A2C-DRL: Dynamic Scheduling for Stochastic Edge\u2013Cloud Environments Using A2C and Deep Reinforcement Learning. IEEE IOT 11 9 (2024).","DOI":"10.1109\/JIOT.2024.3366252"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17088"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Mohit Kumar S.C. Sharma Anubhav Goel and S.P. Singh. 2019. A comprehensive survey for scheduling techniques in cloud computing. JNCA 143 (2019).","DOI":"10.1016\/j.jnca.2019.06.006"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.2737\/FPL-GTR-290"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Zhisheng Ye Wei Gao Qinghao Hu Peng Sun Xiaolin Wang Yingwei Luo Tianwei Zhang and Yonggang Wen. 2024. Deep Learning Workload Scheduling in GPU Datacenters: A Survey. ACM Comput. Surv. 56 6 (2024).","DOI":"10.1145\/3638757"},{"key":"e_1_3_3_2_7_2","volume-title":"Proc. of NSDI","author":"Yang Zongheng","year":"2023","unstructured":"Zongheng Yang, Zhanghao Wu, Michael Luo, Wei-Lin Chiang, Romil Bhardwaj, Woosuk Kwon, Siyuan Zhuang, Frank\u00a0Sifei Luan, Gautam Mittal, Scott Shenker, et\u00a0al. 2023. { SkyPilot} : An intercloud broker for sky computing. In Proc. of NSDI."},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3458336.3465301"},{"key":"e_1_3_3_2_9_2","unstructured":"2024. Banking as a Service (BaaS) Platform Market. https:\/\/www.futuremarketinsights. com \/reports \/banking-as-a-service-baas-platform-market. Accessed in December 2024."},{"key":"e_1_3_3_2_10_2","unstructured":"2024. Changing the game: the impact of artificial intelligence on the banking and capital markets sector. www.deloitte.com\/global\/en\/Industries\/financial-services\/perspectives\/changing-the-game.html. Accessed in December 2024."},{"key":"e_1_3_3_2_11_2","volume-title":"Proc. of OSDI","author":"Xiao Wencong","year":"2018","unstructured":"Wencong Xiao, Romil Bhardwaj, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Zhenhua Han, Pratyush Patel, Xuan Peng, Hanyu Zhao, Quanlu Zhang, Fan Yang, and Lidong Zhou. 2018. Gandiva: Introspective Cluster Scheduling for Deep Learning. In Proc. of OSDI."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2016.57"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS53621.2022.00052"},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Yixin Bao Yanghua Peng and Chuan Wu. 2023. Deep Learning-Based Job Placement in Distributed Machine Learning Clusters With Heterogeneous Workloads. IEEE\/ACM Transactions on Networking 31 2 (2023).","DOI":"10.1109\/TNET.2022.3202529"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Peter Kairouz H\u00a0Brendan McMahan Brendan Avent Aur\u00e9lien Bellet Mehdi Bennis Arjun\u00a0Nitin Bhagoji Kallista Bonawitz Zachary Charles Graham Cormode Rachel Cummings et\u00a0al. 2021. Advances and Open Problems in Federated Learning. ACM Found. Trends Mach. Learn. 14 1\u20132 (2021).","DOI":"10.1561\/2200000083"},{"key":"e_1_3_3_2_16_2","volume-title":"Proc. of AISTATS","author":"Jin Hao","year":"2022","unstructured":"Hao Jin, Yang Peng, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2022. Federated Reinforcement Learning with Environment Heterogeneity. In Proc. of AISTATS."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Zhijie Xie and Shenghui Song. 2023. FedKL: Tackling data heterogeneity in federated reinforcement learning by penalizing KL divergence. IEEE JSAC 41 4 (2023).","DOI":"10.1109\/JSAC.2023.3242734"},{"key":"e_1_3_3_2_18_2","volume-title":"Proc. of ICML","author":"Woo Jiin","year":"2023","unstructured":"Jiin Woo, Gauri Joshi, and Yuejie Chi. 2023. The blessing of heterogeneity in federated Q-learning: linear speedup and beyond. In Proc. of ICML."},{"key":"e_1_3_3_2_19_2","unstructured":"Chenyu Zhang Han Wang Aritra Mitra and James Anderson. 2024. Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning. arXiv (2024)."},{"key":"e_1_3_3_2_20_2","unstructured":"Han Wang Sihong He Zhili Zhang Fei Miao and James Anderson. 2024. Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments. arXiv (2024)."},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Zheyi Chen Jia Hu Geyong Min Chunbo Luo and Tarek El-Ghazawi. 2022. Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning. IEEE TPDS 33 8 (2022).","DOI":"10.1109\/TPDS.2021.3132422"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Tiangang Li Shi Ying Yishi Zhao and Jianga Shang. 2024. Batch Jobs Load Balancing Scheduling in Cloud Computing Using Distributional Reinforcement Learning. IEEE TPDS 35 1 (2024).","DOI":"10.1109\/TPDS.2023.3334519"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796792"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM52122.2024.10621190"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Alysa\u00a0Ziying Tan Han Yu Lizhen Cui and Qiang Yang. 2023. Towards Personalized Federated Learning. IEEE TNNLS 34 12 (2023).","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS57875.2023.00093"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3605573.3605641"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10000980"},{"key":"e_1_3_3_2_29_2","unstructured":"2014. Google 2011 Workloads Archive. https:\/\/github.com\/google\/cluster-data\/. Accessed in July 2024."},{"key":"e_1_3_3_2_30_2","unstructured":"2019. Alibaba Workloads Archive. https:\/\/github.com\/alibaba\/clusterdata\/. Accessed in July 2024."},{"key":"e_1_3_3_2_31_2","unstructured":"2019. Chameleon Cloud Traces. https:\/\/www.scienceclouds.org\/cloud-traces\/. Accessed in July 2024."},{"key":"e_1_3_3_2_32_2","volume-title":"Proc. of JSSPP","author":"Spi\u0161akov\u00e1 Vikt\u00f3ria","year":"2023","unstructured":"Vikt\u00f3ria Spi\u0161akov\u00e1, Dalibor Klus\u00e1\u010dek, and Luk\u00e1\u0161 Hejtm\u00e1nek. 2023. Using Kubernetes in Academic Environment: Problems and Approaches. In Proc. of JSSPP."},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-77398-8_2"},{"key":"e_1_3_3_2_34_2","unstructured":"2021. JSSPP 2021 Workloads Archive. https:\/\/jsspp.org\/workload\/. Accessed in July 2024."},{"key":"e_1_3_3_2_35_2","volume-title":"Proc. of ICLR","author":"Jiang Meirui","year":"2024","unstructured":"Meirui Jiang, Anjie Le, Xiaoxiao Li, and Qi Dou. 2024. Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate. In Proc. of ICLR."},{"key":"e_1_3_3_2_36_2","unstructured":"Yishay Mansour Mehryar Mohri Jae Ro and Ananda Theertha Suresh. 2020. Three Approaches for Personalization with Applications to Federated Learning. arXiv (2020)."},{"key":"e_1_3_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Truc Nguyen and My\u00a0T. Thai. 2024. Preserving Privacy and Security in Federated Learning. IEEE\/ACM Transactions on Networking 32 1 (2024).","DOI":"10.1109\/TNET.2023.3302016"},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","unstructured":"Junqing Le Di Zhang Xinyu Lei Long Jiao Kai Zeng and Xiaofeng Liao. 2023. Privacy-Preserving Federated Learning With Malicious Clients and Honest-but-Curious Servers. IEEE TIFS 18 (2023).","DOI":"10.1109\/TIFS.2023.3295949"},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Yifeng Zheng Shangqi Lai Yi Liu Xingliang Yuan Xun Yi and Cong Wang. 2022. Aggregation service for federated learning: An efficient secure and more resilient realization. IEEE TDSC 20 2 (2022).","DOI":"10.1109\/TDSC.2022.3146448"},{"key":"e_1_3_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS57875.2023.00069"},{"key":"e_1_3_3_2_41_2","unstructured":"Jiaju Qi Qihao Zhou Lei Lei and Kan Zheng. 2021. Federated Reinforcement Learning: Techniques Applications and Open Challenges. arXiv (2021)."},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Muhammed\u00a0Tawfiqul Islam Shanika Karunasekera and Rajkumar Buyya. 2022. Performance and Cost-Efficient Spark Job Scheduling Based on Deep Reinforcement Learning in Cloud Computing Environments. IEEE TPDS 33 7 (2022).","DOI":"10.1109\/TPDS.2021.3124670"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3005745.3005750"},{"key":"e_1_3_3_2_44_2","volume-title":"Proc. of ICML","author":"Marfoq Othmane","year":"2022","unstructured":"Othmane Marfoq, Giovanni Neglia, Richard Vidal, and Laetitia Kameni. 2022. Personalized Federated Learning through Local Memorization. In Proc. of ICML."},{"key":"e_1_3_3_2_45_2","volume-title":"Proc. of ACML","author":"Ma Zichen","year":"2021","unstructured":"Zichen Ma, Yu Lu, Wenye Li, Jinfeng Yi, and Shuguang Cui. 2021. PFedAtt: Attention-based Personalized Federated Learning on Heterogeneous Clients. In Proc. of ACML."},{"key":"e_1_3_3_2_46_2","unstructured":"H.\u00a0Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Ag\u00fcera y Arcas. 2016. Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv (2016)."},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM52122.2024.10621260"},{"key":"e_1_3_3_2_48_2","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. arXiv (2017)."}],"event":{"name":"ICPP '25: 54th International Conference on Parallel Processing","location":"San Diego CA USA","acronym":"ICPP '25"},"container-title":["Proceedings of the 54th International Conference on Parallel Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3754598.3754602","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T08:36:08Z","timestamp":1766219768000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3754598.3754602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":47,"alternative-id":["10.1145\/3754598.3754602","10.1145\/3754598"],"URL":"https:\/\/doi.org\/10.1145\/3754598.3754602","relation":{},"subject":[],"published":{"date-parts":[[2025,9,8]]},"assertion":[{"value":"2025-12-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}