Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:46:55.757872Z
Paper Citation Record · LEDGER
Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee
As of 16 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2505.06651.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
51 of 51 outbound references displayed
- verified exact2
- verified fuzzy42
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
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
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:46:56.137676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7f02f943-1e0b-4e99-a836-f5401a0dfdd4 · outbound
This paper cites Differentially Private Meta-Learning.
Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Differentially Private Meta-Learning
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:46:55.629970Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.