PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training | Paper Citation Record

Observation d9444d54-13cc-43bd-9f66-fd100b13ebda · outbound

This paper cites Gpt-4 Technical Report.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Gpt-4 Technical Report

Reference 1

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Observation 1c41ff2f-4bc5-4352-963b-bbfe9164c39e · outbound

This paper cites Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.462168Z

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Observation 040664a4-1d3e-4029-8c3d-1673019026d8 · outbound

This paper cites Sparse Communication for Distributed Gradient Descent.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Sparse Communication for Distributed Gradient Descent

Reference 3

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Observation a0c7019c-1680-4f85-91d7-b823141e8800 · outbound

This paper cites QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

Reference 4

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Observation b0df2014-5acd-4035-b175-765ee9e4c0ac · outbound

This paper cites The AI Disruption: Challenges and Guidance for Data Center Design.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training The AI Disruption: Challenges and Guidance for Data Center Design

Reference 5

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Observation 62120cb1-655b-4527-a3f4-0b34709603ef · outbound

This paper cites Keyword Transformer: A Self-Attention Model for Keyword Spotting.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Keyword Transformer: A Self-Attention Model for Keyword Spotting

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.399513Z

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Observation 10538ecd-b018-4432-9f3a-74534ce9d53b · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Flower: A Friendly Federated Learning Research Framework

Reference 7

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Observation 828bc4ff-cd68-41b8-a4be-0698d04e8eee · outbound

This paper cites Towards Federated Learning at Scale: System Design.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Towards Federated Learning at Scale: System Design

Reference 8

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Observation d54d3fd5-26e6-4bfc-b59b-e103ad9c3a62 · outbound

This paper cites Leaf: A Benchmark for Federated Settings.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Leaf: A Benchmark for Federated Settings

Reference 9

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Observation c6edfb8d-b72f-412b-8928-d36973caa7e9 · outbound

This paper cites FLAME: Federated Learning across Multi-device Environments.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training FLAME: Federated Learning across Multi-device Environments

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.337799Z

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Observation 8415f408-f770-4991-9e77-04dacf92a2a5 · outbound

This paper cites EMNIST: Extending MNIST to Handwritten Letters.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training EMNIST: Extending MNIST to Handwritten Letters

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.322265Z

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Observation d5484867-ed68-4c17-afe2-7f3bd9861f7a · outbound

This paper cites A Snapshot of the Frontiers of Client Selection in Federated Learning.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training A Snapshot of the Frontiers of Client Selection in Federated Learning

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 893df496-0414-4f08-8228-a68569c1f1ee · outbound

This paper cites Large Scale Distributed Deep Networks.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Large Scale Distributed Deep Networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.293604Z

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Observation 144b08bb-767b-4018-983b-1aeb737d9485 · outbound

This paper cites Exponential Laws of Computing Growth.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Exponential Laws of Computing Growth

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.279068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f83d6067-7a81-411b-93a0-6408522adf79 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training QLoRA: Efficient Finetuning of Quantized LLMs

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.263498Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 39ff463c-611d-4929-b5f4-eb3d60eceb8f · outbound

This paper cites Beyond A Single AI Cluster: A Survey of Decentralized LLM Training.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Beyond A Single AI Cluster: A Survey of Decentralized LLM Training

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.249293Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 09c160de-fe0d-4d2b-8a3e-30e8f6101c91 · outbound

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

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training DiLoCo: Distributed Low-Communication Training of Language Models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.234163Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 63d7a928-e382-4513-8c8f-992d933ef017 · outbound

This paper cites Client Selection in Federated Learning: Principles, Challenges, and Opportunities.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.219626Z

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Observation 0630d78b-8c9a-4d32-9bfb-b6b13cadd60b · outbound

This paper cites TensorFlow Federated: Machine Learning on Decentralized Data.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training TensorFlow Federated: Machine Learning on Decentralized Data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.203012Z

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Observation 558270f2-167c-4498-8fda-cc3239886bf7 · outbound

This paper cites Efficiency: How We Do It.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Efficiency: How We Do It

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.188299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation da967d09-2c40-4731-98a7-6946aa041b5f · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training LoRA: Low-Rank Adaptation of Large Language Models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.172343Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fe846704-0992-4c76-8378-50ffc5009935 · outbound

This paper cites ThunderServe: High-Performance and Cost-Efficient LLM Serving in Cloud Environments.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training ThunderServe: High-Performance and Cost-Efficient LLM Serving in Cloud Environments

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.155589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af67f297-beb7-4bbd-9a6b-a2591f033ea8 · outbound

This paper cites MegaScale: Scaling Large Language Model Training to More than 10,000 GPUs.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training MegaScale: Scaling Large Language Model Training to More than 10,000 GPUs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.138429Z

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Observation 7999ff26-fd8b-4a40-8a3d-95cd3981dbca · outbound

This paper cites Scaling Laws for Neural Language Models.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Scaling Laws for Neural Language Models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.121681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 80c33391-2df9-4504-a4f9-6c5c6685980d · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Federated Learning: Strategies for Improving Communication Efficiency

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.105270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1e4eb50b-50b5-4b16-86fc-8fa4ba84b1f9 · outbound

This paper cites Oort: Efficient Federated Learning via Guided Participant Selection.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Oort: Efficient Federated Learning via Guided Participant Selection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.089527Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff0dc7b1-6bdc-400e-9a43-5e334c73cb2e · outbound

This paper cites Breaking barriers to data center growth.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Breaking barriers to data center growth

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.072650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 281ea7b2-3e2f-4189-a728-bd8fe03e85f9 · outbound

This paper cites Federated Optimization in Heterogeneous Networks.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Federated Optimization in Heterogeneous Networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.057348Z

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Observation ce04022f-f2de-461d-8bb7-68fa27b0051d · outbound

This paper cites Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.041213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 38965539-c5a1-4301-a92d-75187ec5ab81 · outbound

This paper cites Ecolearn: Optimizing the Carbon Footprint of Federated Learning.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Ecolearn: Optimizing the Carbon Footprint of Federated Learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.025542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bc0ab3c4-2094-470a-9b23-bcec9b71a068 · outbound

This paper cites Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:51.010395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9fd656cb-89a2-41dd-96e2-f8332be2ca2f · outbound

This paper cites Rethinking Load Growth: Assessing the Potential for Integration of Large Flexible Loads in US Power Systems.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Rethinking Load Growth: Assessing the Potential for Integration of Large Flexible Loads in US Power Systems

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.994901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6dcd0c25-9f0f-45c7-8aa5-10b2390b12c3 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Carbon Emissions and Large Neural Network Training

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.979700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 267e2e9a-b434-416e-b29f-9371c0b3a002 · outbound

This paper cites Expanding Data Center Capacity to Meet Growing Demand.McKinsey and Company, 2024.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Expanding Data Center Capacity to Meet Growing Demand.McKinsey and Company, 2024

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.964242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1ae588f3-dfab-4257-9a40-52edbbe44175 · outbound

This paper cites A Generic Framework for Privacy Preserving Deep Learning.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training A Generic Framework for Privacy Preserving Deep Learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.949053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c04bc08b-64e8-4ad7-979a-83ab64ba1fbb · outbound

This paper cites AI, Data Centers and the Coming US Power De- mand Surge.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training AI, Data Centers and the Coming US Power De- mand Surge

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.933193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54cef338-5f3c-4ad3-965b-7fa948321ec7 · outbound

This paper cites Data Center Power Oversubscription with a Medium Voltage Power Plane and Priority-Aware Capping.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Data Center Power Oversubscription with a Medium Voltage Power Plane and Priority-Aware Capping

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.918309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7117c599-175e-4b2a-8f71-916db99d85b4 · outbound

This paper cites Photon: Federated LLM Pre-Training.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Photon: Federated LLM Pre-Training

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.902495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0da220d3-807c-4f27-908a-b6696627a1fd · outbound

This paper cites Green AI.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Green AI

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.887858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0108eebd-a6d0-4676-9a60-ff5ebc0e8888 · outbound

This paper cites A Quantitative Survey of Communication Optimizations in Distributed Deep Learning.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training A Quantitative Survey of Communication Optimizations in Distributed Deep Learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.871327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 09aad996-cf2f-47e6-972d-653a6622a386 · outbound

This paper cites GreenLLM: Disaggregating Large Language Model Serving on Heterogeneous GPUs for Lower Carbon Emissions.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training GreenLLM: Disaggregating Large Language Model Serving on Heterogeneous GPUs for Lower Carbon Emissions

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.853505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1fa96db6-5264-441b-9389-9f5748d57fd4 · outbound

This paper cites Democratizing AI: Open-Source Scalable LLM Training on GPU-Based Supercomputers.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Democratizing AI: Open-Source Scalable LLM Training on GPU-Based Supercomputers

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.834091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6cea097d-059c-4634-8e1f-a749208eea7f · outbound

This paper cites ML Training with Cloud GPU Shortages: Is Cross-Region the Answer? In MLSys, 2024.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training ML Training with Cloud GPU Shortages: Is Cross-Region the Answer? In MLSys, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.815463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fc46dfc4-3cb4-445e-ad47-44c9f1be9974 · outbound

This paper cites Fusionllm: A Decentralized LLM Training System on Geo-Distributed GPUs with Adaptive Compression.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Fusionllm: A Decentralized LLM Training System on Geo-Distributed GPUs with Adaptive Compression

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.797546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ee66ed5d-7ed6-43dc-84c6-3dd3cf708d84 · outbound

This paper cites LlaMa 2: Open Foundation and Fine-Tuned Chat Models.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training LlaMa 2: Open Foundation and Fine-Tuned Chat Models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.779285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 21559192-b892-4476-bdf1-d3931fbf7a30 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.762003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1409e08c-79f1-4fb2-a488-448351a653c6 · outbound

This paper cites Sus- tainable AI: Environmental Implications, Challenges and Opportunities.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Sus- tainable AI: Environmental Implications, Challenges and Opportunities

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.746577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b1133fb2-4642-461c-9c3b-1a812004ecb5 · outbound

This paper cites SkyPilot: An Intercloud Broker for Sky Computing.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training SkyPilot: An Intercloud Broker for Sky Computing

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.729341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d7cedff9-b7c9-4daf-b066-9c866d88ab6a · outbound

This paper cites Openfedllm: Training Large Language Models on Decentralized Private Data via Federated Learning.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training Openfedllm: Training Large Language Models on Decentralized Private Data via Federated Learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.711468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d972b638-49f8-4ce0-a530-0214fd934132 · outbound

This paper cites OPT: Open Pre-Trained Transformer Language Models.

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training OPT: Open Pre-Trained Transformer Language Models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:46:50.694184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers