FedBiF: Communication-Efficient Federated Learning via Bits Freezing | Paper Citation Record

Observation cb5e1b2a-dde9-4d53-be94-7af99566f391 · outbound

This paper cites Qsgd: Communication-efficient sgd via gradient quantization and encoding.Advances in neural information processing systems, 30, 2017.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Qsgd: Communication-efficient sgd via gradient quantization and encoding.Advances in neural information processing systems, 30, 2017

Reference 1

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Observation 1b800ef2-8214-49ed-bd55-23c4fa214aab · outbound

This paper cites Federated Learning With Quantized Global Model Updates.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Federated Learning With Quantized Global Model Updates

Reference 2

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Observation 291e0cc5-1fbc-496b-8835-44e2c42cebda · outbound

This paper cites Post training 4- bit quantization of convolutional networks for rapid-deployment.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Post training 4- bit quantization of convolutional networks for rapid-deployment

Reference 3

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Observation 983e4a74-b539-49c0-82c2-d5dc07bf6950 · outbound

This paper cites signsgd with majority vote is communication efficient and fault tolerant.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing signsgd with majority vote is communication efficient and fault tolerant

Reference 4

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Observation d978c4f0-0881-40f6-9b9e-302b55ad8344 · outbound

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

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Flower: A Friendly Federated Learning Research Framework

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Observation 2169e796-281b-4b15-9239-1b099436e3e0 · outbound

This paper cites Large-scale machine learning with stochastic gradient descent.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Large-scale machine learning with stochastic gradient descent

Reference 6

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Observation bd03a2f5-fc22-4f0d-afdb-ea5fe7a39a70 · outbound

This paper cites Adaptive federated dropout: Improving communication efficiency and generalization for federated learning.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Adaptive federated dropout: Improving communication efficiency and generalization for federated learning

Reference 7

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Observation 7b9f66d4-7eb7-40c6-a40d-eb58708b1eb2 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 8

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Observation 2c1d575a-b7bf-4309-b49d-dd72db364ef9 · outbound

This paper cites Bina- ryconnect: Training deep neural networks with binary weights during propagations.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Bina- ryconnect: Training deep neural networks with binary weights during propagations

Reference 9

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Observation c5981372-e255-4145-a9d5-721f48699a3f · outbound

This paper cites Docofl: Downlink compression for cross-device federated learning.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Docofl: Downlink compression for cross-device federated learning

Reference 10

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Observation 38144c52-0dd7-49f3-b004-c24adfa0b8d0 · outbound

This paper cites Esser, Jeffrey L.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Esser, Jeffrey L

Reference 11

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Observation cace2071-10c7-4d29-9d32-5889fb1824d0 · outbound

This paper cites Deep sparse rectifier neural networks.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Deep sparse rectifier neural networks

Reference 12

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Observation 999dd907-04c7-4131-8750-8fb531b0116c · outbound

This paper cites EF21- P and friends: Improved theoretical communication complexity for distributed optimization with bidirectional compression.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing EF21- P and friends: Improved theoretical communication complexity for distributed optimization with bidirectional compression

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Observation 0b09fe6f-b22f-4768-ac25-00d3940c0515 · outbound

This paper cites Quantization robust federated learning for efficient inference on heterogeneous devices.Trans.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Quantization robust federated learning for efficient inference on heterogeneous devices.Trans

Reference 14

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Observation b23efc4b-caa6-42c4-ac3d-683a881c36e6 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 15

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Observation 7c7387c9-1350-43b7-bc17-0131d7e4e2f1 · outbound

This paper cites Deep residual learning for image recognition.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Deep residual learning for image recognition

Reference 16

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Observation 4950f11c-9868-436d-b056-c4f33d820aa5 · outbound

This paper cites Neural networks for machine learning.Coursera video lectures, 2012.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Neural networks for machine learning.Coursera video lectures, 2012

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Observation 715a4304-adcc-4c25-92bd-ac0b5b907a8d · outbound

This paper cites an unresolved cited work.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Unresolved cited work

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Observation fd1d192d-e7bb-4652-9bf0-a0adf1ffa383 · outbound

This paper cites Weinberger.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Weinberger

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Observation 392ccd46-e491-4483-b9ce-5da79ea4e4f0 · outbound

This paper cites Fedpara: Low-rank hadamard product for communication-efficient federated learning.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Fedpara: Low-rank hadamard product for communication-efficient federated learning

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Observation db875a93-8e0b-48fe-88b9-0a4b548b88b9 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Batch normalization: Accelerating deep network training by reducing internal covariate shift

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Observation b07db55e-4d74-40e5-8b8f-b5aee74785b6 · outbound

This paper cites Sparse random networks for communication-efficient federated learning.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Sparse random networks for communication-efficient federated learning

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Observation 24df3766-e8a6-4344-8b3e-fadf1d0fb0b2 · outbound

This paper cites Howard, Hartwig Adam, and Dmitry Kalenichenko.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Howard, Hartwig Adam, and Dmitry Kalenichenko

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Observation 2a655260-0523-45f1-90bc-15bbaad4b9df · outbound

This paper cites an unresolved cited work.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Unresolved cited work

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Observation 75bdbc8d-5fbd-4b6f-81d9-8dab48fa55c4 · outbound

This paper cites Model Pruning Enables Efficient Federated Learning on Edge Devices.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Model Pruning Enables Efficient Federated Learning on Edge Devices

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Observation 0f61389d-c847-4ac2-8ec0-532e771149ea · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Quantizing deep convolutional networks for efficient inference: A whitepaper

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Observation 1d8a3446-95a0-443c-b40e-9d051a7fc284 · outbound

This paper cites Learning multiple layers of features from tiny images.Technical report, University of Toronto, 2009.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Learning multiple layers of features from tiny images.Technical report, University of Toronto, 2009

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Observation 5db430db-ac56-4c78-badd-7e6d50462877 · outbound

This paper cites Tiny imagenet visual recognition challenge,.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Tiny imagenet visual recognition challenge,

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This paper cites Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Unresolved cited work

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This paper cites Federated learning on non-iid data silos: An experimental study.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Federated learning on non-iid data silos: An experimental study

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This paper cites Masked random noise for communication-efficient federated learning.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Masked random noise for communication-efficient federated learning

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Adaptive low-precision training for embeddings in click-through rate prediction

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing The panaceas for improving low-rank decomposition in communication-efficient federated learning

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Fedbat: Communication-efficient federated learning via learnable binarization

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Towards efficient replay in federated incremental learning

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Re- fed+: A better replay strategy for federated incremental learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Unleashing the power of continual learning on non-centralized devices: A survey.IEEE Communications Surveys & Tutorials, 2025

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing FedSSI: Rehearsal-free continual federated learning with synergistic synaptic intelligence

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Personalized federated domain-incremental learning based on adaptive knowledge matching

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Low precision local training is enough for federated learning

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Communication-efficient learning of deep net- works from decentralized data

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Up or down? adaptive rounding for post-training quantization

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Reading digits in natural images with unsupervised feature learning

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Fedaqt: Accurate quantized training with fed- erated learning

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Cocofl: Communication- and computation-aware federated learning via partial NN freezing and quantization.Trans

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Unresolved cited work

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Feddq: Communication-efficient federated learning with descending quantiza- tion

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Fetchsgd: Communication-efficient federated learning with sketching

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Dafkd: Domain-aware federated knowledge distillation

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Decoupling general and personalized knowl- edge in federated learning via additive and low-rank decomposition

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FedBiF: Communication-Efficient Federated Learning via Bits Freezing Bold but cautious: Unlocking the potential of personalized federated learning through cautiously aggressive collaboration

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Observation 66f3c340-ee2b-48c4-8ff9-32c65490547a · outbound

This paper cites The diversity bonus: Learning from dissimilar clients in personalized federated learning.IEEE Transactions on Neural Networks and Learning Systems, 2025.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing The diversity bonus: Learning from dissimilar clients in personalized federated learning.IEEE Transactions on Neural Networks and Learning Systems, 2025

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T18:10:19.603189Z

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Observation 590959ed-c1c1-45b3-a4d5-9925f2f48a01 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T18:10:19.606878Z

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Observation d39daafb-f9d4-4944-82b5-b4cf28df4fa7 · outbound

This paper cites Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Greenewald, Trong Nghia Hoang, and Yasaman Khazaeni

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T18:10:19.610761Z

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Observation 6e13bba0-9dd4-42fb-a34a-634a6e37339d · outbound

This paper cites an unresolved cited work.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing Unresolved cited work

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-04T18:10:18.047240Z

Source-reported events for the cited work

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Observation 432fd320-4e32-49ce-b3e3-a9f23543e00c · outbound

This paper cites His research interests include decision making, deep learning, and optimization algorithms.

FedBiF: Communication-Efficient Federated Learning via Bits Freezing His research interests include decision making, deep learning, and optimization algorithms

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T18:10:19.614372Z

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