Baochun Li
Professor
Studies cloud computing, distributed machine learning systems and federated learning.
Baochun Li's lab at Toronto works on distributed, cloud & edge systems and trustworthy & safe ML. 81 papers since 2023, 15 at top venues, mostly INFOCOM and ACL. Applications close .
At a glance
- papers, 2023–2026
- 21/ year
- at T1 venues
- 19%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage and DBLP, 2023–2026
- 17 papers2026 · T1 3 · collecting
- 10 papers2025 · T1 1
- 33 papers2024 · T1 6
- 21 papers2023 · T1 5
T1 venuesOtherVenues: INFOCOM, ACL, ICDE, AAAI, CIKM
Research focuspaper titles that use each topic's words
- federated learning29 papers
- Distributed, cloud & edge systems5 papers
- Trustworthy & safe ML0 papers
- cloud computing0 papers
- distributed machine0 papers
Paper list
From the lab homepage and DBLP.
202617 papers
- ICDCSClearing MCP Navigation Fog with Economics-Aware Hierarchical Tool Routing (opens in a new tab)
- Baochun Li Project Website Presentation SlidesDays: Discrete-Event Network Simulation on Steroids (opens in a new tab)
- 36468-36481, AugustFedALL: Minimizing Variance of Partial Participation in Federated Learning (opens in a new tab)
- IEEE Trans. Cloud Comput.FedLID: Leveraging Dimensionality to Overcome Noisy Labels in Federated Learning (opens in a new tab)
- ICANNFedReview: Review and Dispose Poisoned Updates Without Validation Datasets or Historic Knowledge (opens in a new tab)
- ACL (T1)Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence (opens in a new tab)
- NOSSDAVIVA: Proactive Bitrate Orchestration for Multiparty Video Conferencing via Conversational Intent (opens in a new tab)
- Project Website Presentation SlidesNextmini: A New Research Testbed for Network Emulation and Experimentation (opens in a new tab)
- ICDCSOptimizing Wide-Area Collective Communication via Flow-Dependency-Aware Rate Allocation (opens in a new tab)
- —PVMark: Enabling Public Verifiability for LLM Watermarking Schemes (opens in a new tab)
- 4589-4604, MarchRevisiting Flow Control in Node-Centric Datacenter Networks (opens in a new tab)
- ACL (T1)rSIM: Incentivizing Reasoning Capabilities of LLMs via Reinforced Strategy Injection (opens in a new tab)
202510 papers
- 7345-7359, AugustA Joint Secure Mechanism of Multi-task Learning For A UAV Team Under FDI Attacks (opens in a new tab)
- ICDCSCommunication-Efficient MoE Fine-Tuning with Locality-Aware Expert Placement (opens in a new tab)
- IEEE Trans. Dependable Secur. Comput.Feature Reconstruction Attacks and Countermeasures of DNN Training in Vertical Federated Learning (opens in a new tab)
- IEEE Trans. Netw.Grace: Toward Routing in Dynamic Network Environments With Graph Embedding (opens in a new tab)
- 3378-3390, DecemberGrace: Towards Routing in Dynamic Network Environments with Graph Embedding (opens in a new tab)
- 5773-5785, September-OctoberGraph-Relational Federated Learning: Enhanced Personalization and Robustness (opens in a new tab)
- CIKM (T1)HyperGenFL: Hypernetwork-Generated Model Aggregation in Federated Learning (opens in a new tab)
- 221-233, January-FebruaryMaskCrypt: Federated Learning with Selective Homomorphic Encryption (opens in a new tab)
- ICCCNPegasus: Efficient Asynchronous Three-Layer Federated Learning (opens in a new tab)
- AsiaCCSUnraveling Elevated Data Leakage in Split Learning for Fine-Tuning Stable Diffusion Models (opens in a new tab)
202433 papers
- MiddlewareAsyncFilter: Detecting Poisoning Attacks in Asynchronous Federated Learning (opens in a new tab)
- IWQoSBlade: Pushing the Performance Envelope of Asynchronous Federated Learning (opens in a new tab)
- ICLR (T1)Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models (opens in a new tab)
- ICDCSCalibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning (opens in a new tab)
- INFOCOM WorkshopCascade: Enhancing Reinforcement Learning with Curriculum Federated Learning and Interference Avoidance — A Case Study in Adaptive Bitrate Selection (opens in a new tab)
- 4122-4132, July-AugustCMI: Client-Targeted Membership Inference in Federated Learning (opens in a new tab)
- —Data Reconstruction and Protection in Federated Learning for Fine-Tuning Large Language Models (opens in a new tab)
- 580-593, MarchDeep Reinforcement Learning Based Dynamic Flowlet Switching for DCN (opens in a new tab)
- —Democratizing the Federation in Federated Learning (opens in a new tab)
- —Differentially Private Dataset Condensation (opens in a new tab)
- 294-300, MarchFederated Unlearning and Its Privacy Threats
- 3067-3075, MarchHarnessing the Power of Local Supervision in Federated Learning (opens in a new tab)
202321 papers
- 515-531, January-MarchAccelerating Distributed Learning in Non-Dedicated Environments (opens in a new tab)
- 1-24, FebruaryAkte-Liquid: Acoustic-Based Liquid Identification with Smartphones (opens in a new tab)
- INFOCOM (T1)Asynchronous Federated Unlearning (opens in a new tab)
- MLSys (T1)Be Careful with PyPI Packages: You May Unconsciously Spread Backdoor Model Weights (opens in a new tab)
- 2353-2365, MarchCharacterizing Performance Limits in Payment Channel Networks (opens in a new tab)
- 2599-2612, MayDifferentially-Private Deep Learning with Directional Noise (opens in a new tab)
- —FedSaw: Communication-Efficient Cross-Silo Federated Learning with Adaptive Compression (opens in a new tab)
- 12679-12692, DecemberFLUID: Towards Efficient Continuous Transaction Processing in DAG-based Blockchains (opens in a new tab)
- IEEE J. Sel. Areas Commun.GIFT: Toward Accurate and Efficient Federated Learning With Gradient-Instructed Frequency Tuning (opens in a new tab)
- 902-914, AprilGIFT: Towards Accurate and Efficient Federated Learning with Gradient-Instructed Frequency Tuning (opens in a new tab)
- 1-23, MayHarmony or Involution: Game Inspiring Age-of-Information Optimization for Data Gathering in IoT (opens in a new tab)
- ACM Trans. Sens. NetworksHarmony or Involution: Game Inspiring Age-of-Information Optimization for Edge Data Gathering in Internet of Things (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
Similar labsnearest by research summary
- CNPeking
Bin Cui
- Efficient training & inference
- CNPeking
Zhen Xiao
- Distributed, cloud & edge systems
- CNSJTU
Chen Chen
- Efficient training & inference
- DETUM
Markus Götz
- Distributed, cloud & edge systems
- CHETH Zurich
Yannis Chronis
- Accelerators, chips & architecture
- SGNUS
Yang You
- Efficient training & inference
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage and DBLP
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
Torontocloses