Chaoyang He

Orcid: 0000-0003-2770-9661

Affiliations:
  • FedML Inc., Los Angeles, CA, USA
  • University of Southern California, Integrated Media Systems Center, Los Angeles, CA, USA (PhD)


According to our database1, Chaoyang He authored at least 57 papers between 2019 and 2024.

Collaborative distances:

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Bibliography

2024
PolyRouter: A Multi-LLM Querying System.
CoRR, 2024

TorchOpera: A Compound AI System for LLM Safety.
CoRR, 2024

LLM Multi-Agent Systems: Challenges and Open Problems.
CoRR, 2024

FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024

TensorOpera Router: A Multi-Model Router for Efficient LLM Inference.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024

2023
L-BGNN: Layerwise Trained Bipartite Graph Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., December, 2023

Accelerated Distributed Approximate Newton Method.
IEEE Trans. Neural Networks Learn. Syst., November, 2023

Partial model averaging in Federated Learning: Performance guarantees and benefits.
Neurocomputing, November, 2023

Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution.
IEEE Trans. Circuits Syst. Video Technol., June, 2023

Achieving small-batch accuracy with large-batch scalability via Hessian-aware learning rate adjustment.
Neural Networks, January, 2023

Distributed Architecture Search Over Heterogeneous Distributions.
Trans. Mach. Learn. Res., 2023

Kick Bad Guys Out! Zero-Knowledge-Proof-Based Anomaly Detection in Federated Learning.
CoRR, 2023

Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory.
CoRR, 2023

FedMLSecurity: A Benchmark for Attacks and Defenses in Federated Learning and LLMs.
CoRR, 2023

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System.
CoRR, 2023

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training.
CoRR, 2023

Federated Analytics: A survey.
CoRR, 2023

A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023


Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain.
Proceedings of the IEEE International Conference on Decentralized Applications and Infrastructures, 2023

FairFed: Enabling Group Fairness in Federated Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities.
IEEE Internet Things Mag., 2022

SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing.
CoRR, 2022

FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

The 1st International Workshop on Federated Learning with Graph Data (FedGraph).
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
AutoCTS: Automated Correlated Time Series Forecasting.
Proc. VLDB Endow., 2021

Advances and Open Problems in Federated Learning.
Found. Trends Mach. Learn., 2021

SPIDER: Searching Personalized Neural Architecture for Federated Learning.
CoRR, 2021

AutoCTS: Automated Correlated Time Series Forecasting - Extended Version.
CoRR, 2021

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks.
CoRR, 2021

Layer-wise Adaptive Model Aggregation for Scalable Federated Learning.
CoRR, 2021

SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision.
CoRR, 2021

LightSecAgg: Rethinking Secure Aggregation in Federated Learning.
CoRR, 2021

A Field Guide to Federated Optimization.
CoRR, 2021

Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection.
CoRR, 2021

SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks.
CoRR, 2021

Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search.
CoRR, 2021

FedNLP: A Research Platform for Federated Learning in Natural Language Processing.
CoRR, 2021

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks.
CoRR, 2021

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers.
CoRR, 2021

Federated Learning for Internet of Things.
Proceedings of the SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems, Coimbra, Portugal, November 15, 2021

MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge.
CoRR, 2020

FedML: A Research Library and Benchmark for Federated Machine Learning.
CoRR, 2020

FedNAS: Federated Deep Learning via Neural Architecture Search.
CoRR, 2020

Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Advances and Open Problems in Federated Learning.
CoRR, 2019

Central Server Free Federated Learning over Single-sided Trust Social Networks.
CoRR, 2019

Efficient Spatial Anti-Aliasing Rendering for Line Joins on Vector Maps.
CoRR, 2019

Adversarial Representation Learning on Large-Scale Bipartite Graphs.
CoRR, 2019

Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling.
Proceedings of the International Joint Conference on Neural Networks, 2019

ADMSv2: A Modern Architecture for Transportation Data Management and Analysis.
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities, 2019


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