25 citations · 91 across the 22 of their papers we have counts for
22 papers
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Sikai Bai, Shuaicheng Li, Weiming Zhuang +7
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fracti…
Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering
Zicong Hong, Song Guo, Enyuan Zhou +5
Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in…
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning
Yingchun Wang, Song Guo, Jingcai Guo +4
Network pruning is a promising way to generate light but accurate models and enable their deployment on resource-limited edge devices. However, the current state-of-the-art assumes…
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning
Xueyang Tang, Song Guo, Jie Zhang
Recently, data heterogeneity among the training datasets on the local clients (a.k.a., Non-IID data) has attracted intense interest in Federated Learning (FL), and many personalize…
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning
Yi Liu, Song Guo, Jie Zhang +3
To eliminate the requirement of fully-labeled data for supervised model training in traditional Federated Learning (FL), extensive attention has been paid to the application of Sel…
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming
Qihua Zhou, Ruibin Li, Song Guo +4
Recent years have witnessed the dramatic growth of Internet video traffic, where the video bitstreams are often compressed and delivered in low quality to fit the streamer's uplink…
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network
Jingcai Guo, Song Guo, Jie Zhang +1
Federated learning (FL) has emerged as a promising privacy-preserving distributed machine learning framework recently. It aims at collaboratively learning a shared global model by…
Layer-wised Model Aggregation for Personalized Federated Learning
Xiaosong Ma, Jie Zhang, Song Guo +1
Personalized Federated Learning (pFL) not only can capture the common priors from broad range of distributed data, but also support customized models for heterogeneous clients. Res…
A Coalition Formation Game Approach for Personalized Federated Learning
Leijie Wu, Song Guo, Yaohong Ding +2
Facing the challenge of statistical diversity in client local data distribution, personalized federated learning (PFL) has become a growing research hotspot. Although the state-of-…
Parameterized Knowledge Transfer for Personalized Federated Learning
Jie Zhang, Song Guo, Xiaosong Ma +3
In recent years, personalized federated learning (pFL) has attracted increasing attention for its potential in dealing with statistical heterogeneity among clients. However, the st…
Federated Unlearning via Class-Discriminative Pruning
Junxiao Wang, Song Guo, Xin Xie +1
We explore the problem of selectively forgetting categories from trained CNN classification models in the federated learning (FL). Given that the data used for training cannot be a…
SDN-based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach
Jun Du, Chunxiao Jiang, Abderrahim Benslimane +2
Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge a…
Personalized Federated Learning with Contextualized Generalization
Xueyang Tang, Song Guo, Jingcai Guo
The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the trainin…
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities
Yuben Qu, Haipeng Dai, Yan Zhuang +4
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering…
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache…
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning
Wenzhuo Yang, Yipeng Zhou, Maio Hu +4
Federated Learning (FL) is an emerging paradigm through which decentralized devices can collaboratively train a common model. However, a serious concern is the leakage of privacy f…
Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
Jiawen Kang, Zehui Xiong, Chunxiao Jiang +6
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" proble…
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases
Huawei Huang, Kangying Lin, Song Guo +2
Although the challenge of the device connection is much relieved in 5G networks, the training latency is still an obstacle preventing Federated Learning (FL) from being largely ado…
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning
Haozhao Wang, Zhihao Qu, Song Guo +3
Federated Learning is a powerful machine learning paradigm to cooperatively train a global model with highly distributed data. A major bottleneck on the performance of distributed…
MAANet: Multi-view Aware Attention Networks for Image Super-Resolution
Jingcai Guo, Shiheng Ma, Song Guo
In most recent years, deep convolutional neural networks (DCNNs) based image super-resolution (SR) has gained increasing attention in multimedia and computer vision communities, fo…
Position-Aware Convolutional Networks for Traffic Prediction
Shiheng Ma, Jingcai Guo, Song Guo +1
Forecasting the future traffic flow distribution in an area is an important issue for traffic management in an intelligent transportation system. The key challenge of traffic predi…
Providing Probabilistic Robustness Guarantee for Crowdsensing
Yuben Qu, Shaojie Tang, Chao Dong +3
Due to its flexible and pervasive sensing ability, crowdsensing has been extensively studied recently in research communities. However, the fundamental issue of how to meet the req…