Paper Citation Record · LEDGER

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.02965.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.02965 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:17:10.156567Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 1081ef37-4f04-41eb-92e4-23bba293fb47 · outbound

This paper cites Petals: Collaborative Inference and Fine-tuning of Large Models.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Petals: Collaborative Inference and Fine-tuning of Large Models

Reference 1

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Observation a2c7c3cf-0d29-4626-abca-ef0fa1486b8c · outbound

This paper cites In: Advances in Neural Information Processing Systems, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Advances in Neural Information Processing Systems, pp

Reference 2

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Observation f53f3e5c-e452-46cd-b070-a6847bc89f8f · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

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This paper cites Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures

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This paper cites DiLoCo: Distributed Low-Communication Training of Language Models.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs DiLoCo: Distributed Low-Communication Training of Language Models

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This paper cites In: International Conference on Machine Learning, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: International Conference on Machine Learning, pp

Reference 6

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This paper cites Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies

Reference 7

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PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Unresolved cited work

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Observation 69d9afbc-4ce4-47c3-b709-652bbb20b523 · outbound

This paper cites 16937–16947 (2020).

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs 16937–16947 (2020)

Reference 9

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PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Unresolved cited work

Reference 10

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Observation 57ff99f8-5996-45f7-bc1f-b8347bbd1f6a · outbound

This paper cites Are We Done with MMLU?.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Are We Done with MMLU?

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This paper cites Training Compute-Optimal Large Language Models.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Training Compute-Optimal Large Language Models

Reference 12

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This paper cites In: Proceedings of Machine Learning and Systems, vol.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Proceedings of Machine Learning and Systems, vol

Reference 13

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This paper cites Mixture of A Million Experts.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Mixture of A Million Experts

Reference 14

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Observation df9181fb-b0b5-40aa-be1d-ff34cd7ccc2d · outbound

This paper cites In: Advances in Neural Information Processing Systems, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Advances in Neural Information Processing Systems, pp

Reference 15

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PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Unresolved cited work

Reference 16

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Observation 7594b3f9-7e5c-48a2-b7fa-3fe7c4d2ea57 · outbound

This paper cites Applied Sciences 11(14), 6421 (2021) https://doi.org/10.3390/app11146421.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Applied Sciences 11(14), 6421 (2021) https://doi.org/10.3390/app11146421

Reference 17

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This paper cites In: Advances in Neural Information Processing Systems, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Advances in Neural Information Processing Systems, pp

Reference 18

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Observation 529f82d9-d3a6-4cfd-ac6c-ae7594b71f1e · outbound

This paper cites Scaling Laws for Neural Language Models.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Scaling Laws for Neural Language Models

Reference 19

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This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 20

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Observation 2f873a68-380c-46a6-9583-85a81d9a8343 · outbound

This paper cites Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients

Reference 21

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This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 22

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PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Artificial Intelligence and Statistics, pp

Reference 23

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This paper cites In: Advances in Neural Information Processing Systems, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Advances in Neural Information Processing Systems, pp

Reference 24

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This paper cites Carbon Emissions and Large Neural Network Training.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Carbon Emissions and Large Neural Network Training

Reference 25

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This paper cites In: IEEE INFOCOM 2024-IEEE Conference on Computer Communications, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: IEEE INFOCOM 2024-IEEE Conference on Computer Communications, pp

Reference 26

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This paper cites In: International Conference on Machine Learning, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: International Conference on Machine Learning, pp

Reference 27

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 28

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Observation 4b8d0850-7f89-443b-9cb9-278cd4d71119 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 29

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This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 30

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This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 31

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This paper cites In: 2017 IEEE Symposium on Security and Privacy (SP), pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: 2017 IEEE Symposium on Security and Privacy (SP), pp

Reference 32

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Observation 79bba974-e0e5-431f-b7fd-71a4374c9c40 · outbound

This paper cites The Computational Limits of Deep Learning.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs The Computational Limits of Deep Learning

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This paper cites IEEE Transactions on Information Forensics and Security 15(8), 3454–3469 (2020) https://doi.org/10.1109/TIFS.2020.2988575.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs IEEE Transactions on Information Forensics and Security 15(8), 3454–3469 (2020) https://doi.org/10.1109/TIFS.2020.2988575

Reference 34

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This paper cites In: 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), pp.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), pp

Reference 35

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This paper cites Emergent Abilities of Large Language Models.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs Emergent Abilities of Large Language Models

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PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: 2024 IEEE Inter- national Parallel and Distributed Processing Symposium (IPDPS), pp

Reference 37

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This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 38

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PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs https://arxiv.org/abs/2505.09343

Reference 39

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This paper cites A Survey on Gradient Inversion: Attacks, Defenses and Future Directions.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs A Survey on Gradient Inversion: Attacks, Defenses and Future Directions

Reference 40

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This paper cites In: Advances in Neural Information Processing Systems (2019) 20.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs In: Advances in Neural Information Processing Systems (2019) 20

Reference 41

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Pith citing papers

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