{"total":37,"rows":[{"id":"2306.01603","title":"Decentralized Federated Learning: A Survey and Perspective","created":"2023-06-02","cited_by_count":86,"area":null},{"id":"2112.11256","title":"Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling","created":"2021-12-21","cited_by_count":66,"area":null},{"id":"2103.10481","title":"Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations","created":"2021-03-18","cited_by_count":51,"area":null},{"id":"2404.06448","title":"Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models","created":"2024-04-09","cited_by_count":49,"area":null},{"id":"2308.13894","title":"FwdLLM: Efficient FedLLM using Forward Gradient","created":"2023-08-26","cited_by_count":26,"area":null},{"id":"2205.10162","title":"FedAdapter: Efficient Federated Learning for Modern NLP","created":"2022-05-20","cited_by_count":25,"area":null},{"id":"2312.00333","title":"Green Edge AI: A Contemporary Survey","created":"2023-12-01","cited_by_count":25,"area":null},{"id":"2302.02573","title":"Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey","created":"2023-02-06","cited_by_count":23,"area":null},{"id":"2106.15734","title":"UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach","created":"2021-06-29","cited_by_count":20,"area":null},{"id":"2212.08343","title":"SplitGP: Achieving Both Generalization and Personalization in Federated Learning","created":"2022-12-16","cited_by_count":20,"area":null},{"id":"2202.02947","title":"Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks","created":"2022-02-07","cited_by_count":18,"area":null},{"id":"2212.05974","title":"Federated Few-Shot Learning for Mobile NLP","created":"2022-12-12","cited_by_count":15,"area":null},{"id":"2106.11814","title":"Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective","created":"2021-06-22","cited_by_count":14,"area":null},{"id":"2205.01557","title":"Training Mixed-Domain Translation Models via Federated Learning","created":"2022-05-03","cited_by_count":12,"area":null},{"id":"2405.09839","title":"Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations","created":"2024-05-16","cited_by_count":12,"area":null},{"id":"2207.03681","title":"A Survey on Participant Selection for Federated Learning in Mobile Networks","created":"2022-07-08","cited_by_count":11,"area":null},{"id":"2203.13950","title":"Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point","created":"2022-03-26","cited_by_count":10,"area":null},{"id":"2301.03062","title":"AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices","created":"2023-01-08","cited_by_count":10,"area":null},{"id":"2307.00777","title":"GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds","created":"2023-07-03","cited_by_count":10,"area":null},{"id":"2307.04338","title":"Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey","created":"2023-07-10","cited_by_count":10,"area":null},{"id":"2303.12414","title":"Delay-Aware Hierarchical Federated Learning","created":"2023-03-22","cited_by_count":9,"area":null},{"id":"2404.13804","title":"Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks","created":"2024-04-22","cited_by_count":9,"area":null},{"id":"2304.12422","title":"Multi-Source to Multi-Target Decentralized Federated Domain Adaptation","created":"2023-04-24","cited_by_count":6,"area":null},{"id":"2112.06135","title":"Communication-Efficient Federated Learning for Neural Machine Translation","created":"2021-12-12","cited_by_count":5,"area":null},{"id":"2208.02856","title":"Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange","created":"2022-08-04","cited_by_count":5,"area":null},{"id":"2209.01750","title":"Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources","created":"2022-09-05","cited_by_count":5,"area":null},{"id":"2212.00192","title":"Towards Practical Few-shot Federated NLP","created":"2022-12-01","cited_by_count":5,"area":null},{"id":"2302.00106","title":"Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling","created":"2023-01-31","cited_by_count":4,"area":null},{"id":"2305.11537","title":"Trustworthy Federated Learning: A Survey","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2305.13503","title":"Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization","created":"2023-05-22","cited_by_count":3,"area":null},{"id":"2409.17189","title":"Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks","created":"2024-09-25","cited_by_count":3,"area":null},{"id":"2203.12738","title":"Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing","created":"2022-03-23","cited_by_count":2,"area":null},{"id":"2305.01154","title":"FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer","created":"2023-05-02","cited_by_count":2,"area":null},{"id":"2311.04350","title":"Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks","created":"2023-11-07","cited_by_count":2,"area":null},{"id":"2302.12305","title":"Coded Matrix Computations for D2D-enabled Linearized Federated Learning","created":"2023-02-23","cited_by_count":0,"area":null},{"id":"2404.09861","title":"Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels","created":"2024-04-15","cited_by_count":0,"area":null},{"id":"2407.00943","title":"FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection","created":"2024-07-01","cited_by_count":0,"area":null}],"next_cursor":null}