Welcome to Federated Learning Seminar :)
| 2021 Fall | ||||||
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| Time: 15:00 - 17:00, Thursday Venue: B914, Science Building | ||||||
| Month | Day | Week | Topic | Speaker | Materials | |
| June | 29 | 0 | A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing | 江宇辉 | Slides | |
| PACE：Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery | 毛炜 | Slides | ||||
| September | 16 | 2 | Adaptive User-managed Service Placement for Mobile Edge Computing An Online Learning Approach | 江宇辉 | Slides | |
| Energy-aware Resource Management | 毛炜 | Slides | ||||
| September | 23 | 3 | In-Edge AI Intelligentizing Mobile Edge Computing Caching and Communication by Federated Learning | 江宇辉 | Slides | |
| Attention-Weighted Federated Deep Reinforcement learning for device-to-device assisted heterogeneous collaborative edge computing | 毛炜 | Slides | ||||
| September | 30 | 4 | Federated Learning with Mutually Cooperating Devices A Consensus Approach Towards Server-Less Model Optimization | 江宇辉 | Slides | |
| An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee | 毛炜 | Slides | ||||
| October | 14 | 5 | Communication-efficient asynchronous federated learning in resource-constrained edge computing | 江宇辉 | Slides | |
| October | 21 | 6 | FedSiam: Towards Adaptive Federated Semi-Supervised Learning | 江宇辉 | Slides | |
| November | 3 | 7 | Asynchronous federated learning | 江宇辉 | Slides | |
| Deep Reinforcement Learning in Federated Learning | 毛炜 | Slides | ||||
| November | 11 | 8 | SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead | 江宇辉 | Slides | |
| November | 18 | 9 | FedAT:A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers | 江宇辉 | Slides | |
| Experience-Driven Computional Resource Allocation of Federated Learning by Deep Reinforcement Learning | 毛炜 | Slides | ||||
| December | 9 | 10 | Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update And Temporally Weighted Aggregation | 江宇辉 | Slides | |
| Resource Allocation in Mobility-Aware Federated Learning | 毛炜 | Slides | ||||
| December | 23 | 11 | Federated Learning under Arbitrary Communication Patterns | 江宇辉 | Slides | |
| Double Deep Reinforcement Learning Scheduling for IoT Devices | 毛炜 | Slides | ||||
| 2021 Spring | ||||||
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| Time: 13:00 - 16:00, Friday Venue: B1002, Science Building | ||||||
| Month | Day | Week | Topic | Speaker | Materials | |
| March | 9 | 2 | Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data |
步一凡 | Slides | |
| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | 郑海坤 | Slides | ||||
| 2020 Fall | ||||||
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| Time: 13:30 - 15:00, Tuesday Venue: B914, Science Building | ||||||
| Month | Day | Week | Topic | Speaker | Materials | |
| September | 15 | 1 | Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems | 步一凡 | Slides | |
| 22 | 2 | FedSmart: An Auto Updating Federated Learning Optimization Mechanism | 步一凡 | Slides | ||
| October | 13 | 5 | OpenEI: An Open Framework for Edge Intelligence | 郑海坤 | Slides | |
| 20 | 6 | Low Rank Communication for Federated Learning | 步一凡 | Slides | ||
| Adaptive Federated Learning in Resource Constrained Edge Computing Systems | 郑海坤 | Slides | ||||
| 27 | 7 | Federated Learning: Strategies for Improving Communication Efficiency | 步一凡 | Slides | ||
| Federated learning with adaptive communication compression under dynamic bandwidth and unreliable networks | 郑海坤 | Slides | ||||
| November | 3 | 8 | Asynchronous Federated Optimization | 步一凡 | Slides | |
| Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud | 郑海坤 | Slides | ||||
| 10 | 9 | Personalized Federated Learning with Moreau Envelopes | 步一凡 | Slides | ||
| Federated Learning with Personalization Layers | 步一凡 | Slides | ||||
| 17 | 10 | Distributed Machine Learning through Heterogeneous Edge Systems | 郑海坤 | Slides | ||
| 24 | 11 | Communication-Efficient On-Device Machine LearningFederated Distillation and Augmentationunder Non-IID Private Data | 步一凡 | Slides | ||
| Decemeber | 1 | 12 | FedMD Heterogenous Federated Learning via Model Distillation | 步一凡 | Slides | |
| 8 | 13 | GAN Enhanced Membership Inference A PassiveLocal Attack in Federated Learning | 步一凡 | Slides | ||
| 15 | 14 | On-Edge Multi-Task Transfer Learning: Model and Practice With Data-Driven Task Allocation | 郑海坤 | Slides | ||
| 22 | 15 | An Efficient Framework for Clustered Federated Learning | 步一凡 | Slides | ||
| 29 | 16 | Optimize Scheduling of Federated Learning on Battery-powered Mobile Devices | 郑海坤 | Slides | ||
| January | 5 | 17 | TiFL: A Tier-based Federated Learning System | 郑海坤 | Slides | |
| 2019 Fall | ||||||
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| Time: 13:00 - 15:00, Friday Venue: 201, Math Building | ||||||
| Month | Day | Week | Topic | Speaker | Materials | |
| November | 22 | 12 | Agnostic Federated Learning | 程俊宏 | Slides | |
| 2019 Spring | ||||||
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| Time: 8:30 - 11:30, Thursday Venue: 201, Math Building | ||||||
| Month | Day | Week | Topic | Speaker | Materials | |
| March | 21 | 5 | 联邦机器学习 | 董天文 | Slides | |
| 28 | 6 | 联邦学习技术及数据隐私保护 | 刘文炎 | Slides | ||
| Federated Transfer Learning | 程俊宏 | Slides | ||||
| 联邦学习中的博弈论 | 李靖东 | Slides | ||||
| 网络安全与数据保护的立法和实践 | Slides | |||||
| 联邦学习：AI For Everyone 的必经之路 | 林淳波 | Slides | ||||
| 城市计算与跨域学习联合建模 | 董天文 | Slides | ||||
| August | 1 | / | 联邦学习拾遗 | 刘文炎 | Slides | |