Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee | Paper Citation Record

Observation 105fcaa7-e5ef-4e31-be79-2492ebe94fc4 · outbound

This paper cites Deep learning with differential privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Deep learning with differential privacy

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.487597Z

Source-reported events for the cited work

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Observation 7a8e9e77-ca57-4f50-8ccc-2bfbcaafd304 · outbound

This paper cites LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:46:55.876314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation adde7926-fb00-4e0f-9b9f-20d37db0e5b5 · outbound

This paper cites Gaussian Differential Privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Gaussian Differential Privacy

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:55.573030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da0b32bc-1e00-4a8b-8670-3e7474f0d29c · outbound

This paper cites Dynamic differential-privacy preserving sgd.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Dynamic differential-privacy preserving sgd

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.416449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 562a9478-08a8-4164-9d73-bed4496b7d52 · outbound

This paper cites Our data, ourselves: Privacy via distributed noise generation.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Our data, ourselves: Privacy via distributed noise generation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.402768Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a895dfc-1a6b-42a0-9ea7-2ed1f333f1a5 · outbound

This paper cites Towards practical differentially private convex optimiza- tion.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Towards practical differentially private convex optimiza- tion

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.339156Z

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Observation 501e01cd-61f9-49e8-ae35-caaafb7516da · outbound

This paper cites Gossip-based computation of aggregate information.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Gossip-based computation of aggregate information

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.325948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4ec16b8d-cdab-4ef9-bf00-7b9d65de5b14 · outbound

This paper cites Learning multiple lay- ers of features from tiny images.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Learning multiple lay- ers of features from tiny images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.299701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb1480b0-901f-4970-82b9-0ae4014ff85e · outbound

This paper cites Convergence and privacy of decentralized nonconvex optimization with gradient clipping and communication compression.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Convergence and privacy of decentralized nonconvex optimization with gradient clipping and communication compression

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.272775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a2218972-7752-4fc7-a8f7-3ee6901d663b · outbound

This paper cites Asynchronous Federated Learning with Differential Privacy for Edge Intelligence.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Asynchronous Federated Learning with Differential Privacy for Edge Intelligence

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:46:55.827781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f97c57a8-ab1a-4282-bcb1-841604dd516a · outbound

This paper cites SoteriaFL: A unified framework for private feder- ated learning with communication compression.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee SoteriaFL: A unified framework for private feder- ated learning with communication compression

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.259572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6952e0e7-c4e7-46f3-ae80-66fd2b24200b · outbound

This paper cites Can decentral- ized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient de- scent.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Can decentral- ized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient de- scent

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.246283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 65be5dbb-5523-4d58-910c-25865afaf606 · outbound

This paper cites Loss-privacy tradeoff in federated edge learning.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Loss-privacy tradeoff in federated edge learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.219295Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 422093e0-9bf0-4f3f-b91b-51c1419bbc09 · outbound

This paper cites Learning Differentially Private Recurrent Language Models.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Learning Differentially Private Recurrent Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:55.656469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b48ee46-73b4-4975-b036-47ed647870a0 · outbound

This paper cites R´enyi differential privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee R´enyi differential privacy

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.204611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40db45dd-e95a-47ac-a4d9-7006f8db4eda · outbound

This paper cites Pytorch: Tensors and dy- namic neural networks in python with strong gpu acceler- ation.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Pytorch: Tensors and dy- namic neural networks in python with strong gpu acceler- ation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.190874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3fddfc44-2382-4bd9-9b4a-4db9beb6d4dd · outbound

This paper cites Privacy enhanced matrix factor- ization for recommendation with local differential privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Privacy enhanced matrix factor- ization for recommendation with local differential privacy

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.177757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 695f0dea-7154-4f37-acab-f5a9f4a02250 · outbound

This paper cites D2: Decentralized training over de- centralized data.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee D2: Decentralized training over de- centralized data

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.163457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a563ec68-06e3-41b3-8ed5-f2017104edbb · outbound

This paper cites Tailoring gradient methods for differentially private distributed optimization.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Tailoring gradient methods for differentially private distributed optimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.150409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 237a9d1b-1464-47cf-86d9-405853ce5549 · outbound

This paper cites Efficient privacy- preserving stochastic nonconvex optimization.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Efficient privacy- preserving stochastic nonconvex optimization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.124808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation da07162a-4ead-4b57-a0e2-fc440e611e01 · outbound

This paper cites Beyond inferring class representatives: User-level privacy leakage from federated learning.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Beyond inferring class representatives: User-level privacy leakage from federated learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.112267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e405470-5c57-4972-a911-054b61bb11db · outbound

This paper cites On differentially private stochas- tic convex optimization with heavy-tailed data.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee On differentially private stochas- tic convex optimization with heavy-tailed data

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.099494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68bbf258-2cbe-402c-a44e-e25067033d98 · outbound

This paper cites Gradient leakage attack resilient deep learning.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Gradient leakage attack resilient deep learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.086576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0fbea95c-27de-420b-a96e-952a4fb6284d · outbound

This paper cites Federated learning with dif- ferential privacy: Algorithms and performance analysis.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Federated learning with dif- ferential privacy: Algorithms and performance analysis

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.073554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0fed96c7-7803-4a27-80ef-ab0fc52d6e39 · outbound

This paper cites Securing distributed sgd against gradient leakage threats.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Securing distributed sgd against gradient leakage threats

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.060225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f91fb697-bf5c-4276-96f4-7455e33ee800 · outbound

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

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:55.716259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b0ac10-6941-459a-b8f8-7a83fb691eb9 · outbound

This paper cites A(DP)ˆ2SGD: Asynchronous decentralized parallel stochastic gradient descent with differential privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee A(DP)ˆ2SGD: Asynchronous decentralized parallel stochastic gradient descent with differential privacy

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.032879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1470db64-ae4a-4d06-9f69-1d6abcec37ea · outbound

This paper cites Decentralized parallel sgd with privacy preserva- tion in vehicular networks.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Decentralized parallel sgd with privacy preserva- tion in vehicular networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.019019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7842d44d-ef31-4b56-9652-7e7d646be487 · outbound

This paper cites Differentially private federated tempo- ral difference learning.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Differentially private federated tempo- ral difference learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.005808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d2f4dabf-c4ab-428d-aaac-7e8cc540af93 · outbound

This paper cites Efficient private erm for smooth objec- tives.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Efficient private erm for smooth objec- tives

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:55.992487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1d3c045-99e6-4ec1-9cec-f3f6eac8b485 · outbound

This paper cites Optimizing the numbers of queries and replies in convex federated learn- ing with differential privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Optimizing the numbers of queries and replies in convex federated learn- ing with differential privacy

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:55.978293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c1311217-54f4-4eb3-a49b-9308e1e22fef · outbound

This paper cites Deep leakage from gradients.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Deep leakage from gradients

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:55.962977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aa691865-ac74-4ac0-989a-e90ba7d36ebb · outbound

This paper cites R-FAST: Robust fully-asynchronous stochastic gradient tracking over general topology.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee R-FAST: Robust fully-asynchronous stochastic gradient tracking over general topology

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:55.948948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8f287718-6e90-4157-b9c2-edd0d12803d9 · outbound

This paper cites Parallelized stochastic gra- dient descent.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Parallelized stochastic gra- dient descent

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:55.935032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 91ddb3e0-2666-43ad-8488-d38f5c1da2b4 · outbound

This paper cites Then, we have kX l=0 λk−lvl !2 ⩽ 1 1−λ kX l=0 λk−l vl 2.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Then, we have kX l=0 λk−lvl !2 ⩽ 1 1−λ kX l=0 λk−l vl 2

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:55.920789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation af01cc66-8ea5-42c1-8efa-e8395830de46 · outbound

This paper cites an unresolved cited work.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:46:55.905958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c5cc06f1-128e-46f8-af13-d696ee72c312 · outbound

This paper cites Decentralized deep learning with arbitrary communication compression.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Decentralized deep learning with arbitrary communication compression

Reference 2003

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.313145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation afdbf54d-a687-4f43-8f73-eee5a4d0cbf9 · outbound

This paper cites The algorithmic foundations of differential privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee The algorithmic foundations of differential privacy

Reference 2006

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.389784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 90d55907-b258-4c9f-bd09-8a8cbd27ae44 · outbound

This paper cites Distributed training of deep learn- ing models: A taxonomic perspective.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Distributed training of deep learn- ing models: A taxonomic perspective

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.285571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a6a2ad2-5414-4491-9a2e-4888de56ff9a · outbound

This paper cites Adap DP- FL: Differentially private federated learning with adaptive noise.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Adap DP- FL: Differentially private federated learning with adaptive noise

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.375400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c6e69a7-0ee2-4e3e-8b64-eeb5b5aeeac9 · outbound

This paper cites Deep residual learning for image recog- nition.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Deep residual learning for image recog- nition

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:55.600183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaba3338-3e5d-4766-ba06-af7d3c65944a · outbound

This paper cites Differentially pri- vate learning with adaptive clipping.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Differentially pri- vate learning with adaptive clipping

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.473572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e5cc428-7680-42e6-a2db-f625b3e6be41 · outbound

This paper cites Asynchronous decentralized parallel stochastic gra- dient descent.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Asynchronous decentralized parallel stochastic gra- dient descent

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.232616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7fe1ca3f-397b-4d20-9828-082e99e88182 · outbound

This paper cites Towards decentralized deep learning with differen- tial privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Towards decentralized deep learning with differen- tial privacy

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.432045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b173630c-3a7e-414f-8fc2-c0eab08ae30c · outbound

This paper cites Deep Learning with Gaussian Differential Privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Deep Learning with Gaussian Differential Privacy

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:55.554181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 211d1d9b-edeb-4a69-83d2-98346724125c · outbound

This paper cites Understanding gradient clipping in private sgd: A geometric perspective.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Understanding gradient clipping in private sgd: A geometric perspective

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.446403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f7a4800-51ec-4e53-bfe0-d2e3e5f15dfe · outbound

This paper cites Stochastic gradient push for distributed deep learning.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Stochastic gradient push for distributed deep learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.460264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dd035b4e-af3b-4c0c-b540-1931cdbbc83d · outbound

This paper cites Escaping from saddle points—online stochastic gra- dient for tensor decomposition.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Escaping from saddle points—online stochastic gra- dient for tensor decomposition

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.361642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2208ca7c-6f21-4f94-8777-bc19c69fe3b7 · outbound

This paper cites The value of collaboration in convex machine learning with differential privacy.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee The value of collaboration in convex machine learning with differential privacy

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:46:56.047058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cbebcc14-d4ba-4555-a5dd-65bac401e516 · outbound

This paper cites Differentially private empirical risk minimization revis- ited: Faster and more general.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Differentially private empirical risk minimization revis- ited: Faster and more general

Reference 2024

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Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Differentially Private Meta-Learning

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