Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T19:44:17.278048Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 1 inbound Pith citation observation for arXiv:2603.01730.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T19:44:17.278048Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-02T19:44:47.733008Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T19:47:18.784816Z
66 of 66 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e317d730-dc85-47cb-841a-0fe3c19d3b64 · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges,
Reference 1
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Observation d71a689e-aff2-4d24-bcc8-d6e3c8b7345f · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized federated learning: A survey on security and privacy,
Reference 2
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Observation 846bf843-a398-472e-8358-930fa7aefe99 · outbound
Decentralized Federated Learning by Partial Message Exchange An innovative decentralized and distributed deep learning framework for predictive maintenance in the industrial internet of things,
Reference 3
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Observation fe432c74-6201-4022-acb9-aa52cc109df4 · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized navigation with heterogeneous federated reinforcement learning for uav- enabled mobile edge computing,
Reference 4
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Observation 0e5faf2f-83d2-4131-b6d0-09b93103bebe · outbound
Decentralized Federated Learning by Partial Message Exchange Federated learning-based energy fore- casting and trading platform for decentralized renewable energy markets,
Reference 5
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Observation c41525aa-662b-4921-b44e-44d14ba71d19 · outbound
Decentralized Federated Learning by Partial Message Exchange Federated learning meets blockchain in decentralized data sharing: Healthcare use case,
Reference 6
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Observation fbd0efca-23d6-404c-8f07-2b387cdb5bda · outbound
Decentralized Federated Learning by Partial Message Exchange Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,
Reference 7
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Observation 5afbc594-5f1c-4494-a792-78ee9a64692c · outbound
Decentralized Federated Learning by Partial Message Exchange Improving the model consistency of decentral- ized federated learning,
Reference 8
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Observation 4340a924-59b7-48cb-9f29-d33f2b678e88 · outbound
Decentralized Federated Learning by Partial Message Exchange Topology-aware generalization of decentralized sgd,
Reference 9
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Observation 227b8c1e-1bfd-4142-bfd2-122873bfe284 · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized optimiza- tion with edge sampling,
Reference 10
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Observation 2e081d8a-c13e-4aef-ab90-a36f6f9d27d1 · outbound
Decentralized Federated Learning by Partial Message Exchange De V os, S
Reference 11
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Observation a2e9a098-82b5-4599-b2a1-21dfed5009a8 · outbound
Decentralized Federated Learning by Partial Message Exchange Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning
Reference 12
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Observation fee3b85f-ee23-447a-815b-6590546d16d8 · outbound
Decentralized Federated Learning by Partial Message Exchange Personalized decentralized federated learning: A privacy-enhanced and byzantine-resilient approach,
Reference 13
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Observation 44bd6a92-fbe4-4ea5-bf9c-a895109d4180 · outbound
Decentralized Federated Learning by Partial Message Exchange Distributed subgradient methods for multi-agent optimization,
Reference 14
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Observation 0e93380b-2867-4772-9116-ce1d12c661e4 · outbound
Decentralized Federated Learning by Partial Message Exchange Com- munication compression for decentralized training,
Reference 15
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Observation bb35fc2b-7b3d-4503-9044-00456717a207 · outbound
Decentralized Federated Learning by Partial Message Exchange The convergence of spar- sified gradient methods,
Reference 16
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Observation d0132d68-9777-418b-82e1-a64869dcbf85 · outbound
Decentralized Federated Learning by Partial Message Exchange Sparse decen- tralized federated learning,
Reference 17
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Observation acf84ca3-5627-4c76-9cda-a39fb841289d · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized learning with unreliable communications,
Reference 18
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Observation 64a6e3d2-31c4-489c-ab82-eb5f8165e58d · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized multi-client functional encryption for in- ner product with applications to federated learning,
Reference 19
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Observation fdd04d28-4fd7-4aa0-9f0f-44883eafd0f3 · outbound
Decentralized Federated Learning by Partial Message Exchange Securing decentral- ized federated learning: cryptographic mechanisms for privacy and trust,
Reference 20
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Observation f7110104-433c-413b-943b-967118060ac2 · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized nonconvex opti- mization with guaranteed privacy and accuracy,
Reference 21
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Observation 361f2988-14a1-4e58-b86d-94eba9ee8427 · outbound
Decentralized Federated Learning by Partial Message Exchange Differentially private linearized admm algorithm for decentralized nonconvex optimization,
Reference 22
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Observation 2624b4ed-b919-431a-9a7c-f327a7a81cf0 · outbound
Decentralized Federated Learning by Partial Message Exchange Dp-norm: Differential privacy primal-dual algorithm for decen- tralized federated learning,
Reference 23
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Observation 94778d74-2ee5-44a6-bf90-13a622de9f44 · outbound
Decentralized Federated Learning by Partial Message Exchange Federated learning via inexact admm,
Reference 24
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Observation f6440328-ffe3-4b4d-a2f3-7e31e97b0808 · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized federated averaging,
Reference 25
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Observation 98a6c711-a798-49b2-b074-1d4300390a66 · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized feder- ated learning: Balancing communication and computing costs,
Reference 26
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Observation 29c7e558-67e5-4842-aaec-f5c8fab44104 · outbound
Decentralized Federated Learning by Partial Message Exchange De- centralized deep learning with arbitrary communication compression,
Reference 27
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Observation 304b8965-43b8-477c-af3c-49e83ecf9e61 · outbound
Decentralized Federated Learning by Partial Message Exchange Squarm- sgd: Communication-efficient momentum sgd for decen- tralized optimization,
Reference 28
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Observation 71222f06-90fd-4d96-8520-34721017bb3c · outbound
Decentralized Federated Learning by Partial Message Exchange Beer: Fasto(1/t)rate for decentralized nonconvex optimiza- tion with communication compression,
Reference 29
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Observation 1110c756-9885-449c-b1b5-d39b9bfbdcc0 · outbound
Decentralized Federated Learning by Partial Message Exchange Decentralized sum-of- nonconvex optimization,
Reference 30
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Observation eabae8b9-e2db-4274-a8c3-87c50490e754 · outbound
Decentralized Federated Learning by Partial Message Exchange A decentralized proximal- gradient method with network independent step-sizes and separated convergence rates,
Reference 31
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Observation 39ba6f39-08df-4b37-9c3b-65a2e18d5935 · outbound
Decentralized Federated Learning by Partial Message Exchange Finite-bit quantization for distributed algorithms with linear con- vergence,
Reference 32
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Observation 63a44a53-a2f4-402d-b4a5-c714b2c35988 · outbound
Decentralized Federated Learning by Partial Message Exchange Private and communication-efficient edge learning: A sparse differ- ential gaussian-masking distributed sgd approach,
Reference 33
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Observation 1c5b90d1-6125-426d-832f-6aaad24d8921 · outbound
Decentralized Federated Learning by Partial Message Exchange Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy,
Reference 34
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Observation f72c6d89-e6d7-4d8a-9583-1990487e4095 · outbound
Decentralized Federated Learning by Partial Message Exchange Communication-Efficient Secure Aggregation in Decentralized Learning
Reference 35
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Observation 319a436d-7a04-4b57-95df-b5b192e12852 · outbound
Decentralized Federated Learning by Partial Message Exchange Numerical optimization,
Reference 36
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Observation 5344d6cd-06ea-42b2-b472-d010634e1085 · outbound
Decentralized Federated Learning by Partial Message Exchange Asynchronous Stochastic Gradient Descent with Delay Compensation
Reference 37
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Observation 60d1e45d-4a15-4326-8bba-459faee91731 · outbound
Decentralized Federated Learning by Partial Message Exchange Parallel restarted sgd with faster convergence and less communication: Demystify- ing why model averaging works for deep learning,
Reference 38
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Observation e40e6986-627a-4445-9258-6a7db098ba00 · outbound
Decentralized Federated Learning by Partial Message Exchange Cooperative sgd: A unified framework for the design and analysis of local-update sgd algorithms,
Reference 39
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Observation ee6d1cc0-115e-4293-856f-55720fcfaa93 · outbound
Decentralized Federated Learning by Partial Message Exchange Communication-efficient learning of deep networks from decentralized data,
Reference 40
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Observation ad3bd8f1-5e8d-41e9-9ed5-6780b3adddfd · outbound
Decentralized Federated Learning by Partial Message Exchange On the Convergence of FedAvg on Non-IID Data
Reference 41
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Observation 800f2dfe-690f-4e0e-a2a9-bd5b74ed2c30 · outbound
Decentralized Federated Learning by Partial Message Exchange Federated optimization in heterogeneous networks,
Reference 42
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Observation 70d04a0f-a8d4-47b3-8d2d-bf9b23b43ecf · outbound
Decentralized Federated Learning by Partial Message Exchange Fedgia: An efficient hybrid algorithm for federated learning,
Reference 43
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Observation cf4f2841-1ac9-411e-80cf-ab2156e68a21 · outbound
Decentralized Federated Learning by Partial Message Exchange The sparse vector technique, revisited,
Reference 44
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Observation 294daf0e-c19b-48c9-a71c-5d2c865687bc · outbound
Decentralized Federated Learning by Partial Message Exchange Improving sparse vector tech- nique with renyi differential privacy,
Reference 45
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Observation a6c94fa2-8306-4b37-90a6-1247c5842f47 · outbound
Decentralized Federated Learning by Partial Message Exchange Secure federated averaging algorithm with differential privacy,
Reference 46
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Observation ad5e005d-8525-446e-b767-a0ba58e55ab3 · outbound
Decentralized Federated Learning by Partial Message Exchange Federated Stochastic Primal-dual Learning with Differential Privacy
Reference 47
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Observation c89e36ad-1808-4ff5-8bdc-0fca144ac977 · outbound
Decentralized Federated Learning by Partial Message Exchange Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Reference 48
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Observation ff3b4b19-4113-4434-9224-e135f745f058 · outbound
Decentralized Federated Learning by Partial Message Exchange Deep residual learning for image recognition,
Reference 49
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Observation b02239fb-fa11-4900-b9e3-d2cc7c8022b8 · outbound
Decentralized Federated Learning by Partial Message Exchange Learning multiple layers of features from tiny images,
Reference 50
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Observation 55366b6d-5c09-4016-84ce-af9eeb8c26bc · outbound
Decentralized Federated Learning by Partial Message Exchange This is adopted for analytical convenience without loss of generality
Reference 51
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Observation ab17072d-bd76-40af-adf2-821e5fa38ca6 · outbound
Decentralized Federated Learning by Partial Message Exchange That is, at every iteration, nodeiselects the same number of neighbor nodes to join in the training
Reference 52
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Observation 9836d4c0-ad13-4844-b730-ec1807e673c8 · outbound
Decentralized Federated Learning by Partial Message Exchange Again, this is adopted for analytical convenience without loss of generality
Reference 53
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Observation 0dcc4eff-5f34-4424-a07a-931750f19c7c · outbound
Decentralized Federated Learning by Partial Message Exchange wk iℓ − 1 r X j∈N k i,ℓ wk jℓ !2 λk i,ℓ =r # +P(λ k i,ℓ = 0)E h (wk iℓ −w k iℓ)2 λk i,ℓ = 0 i = tiX r=1 P(λk i,ℓ =r)E
Reference 54
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Observation 1ec003d7-8810-4f7e-99be-5438e2d79fc0 · outbound
Decentralized Federated Learning by Partial Message Exchange Unresolved cited work
Reference 55
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Observation 69fd5488-e515-4f53-94e7-6e9c881531b6 · outbound
Decentralized Federated Learning by Partial Message Exchange Proof.Since 1) has been shown in Lemma 3, we only prove 2)
Reference 56
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Observation 4002e7f2-c091-41a9-b914-653aeccc28ff · outbound
Decentralized Federated Learning by Partial Message Exchange Unresolved cited work
Reference 57
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Observation d2728ad6-2197-451f-8e24-ab5edc567075 · outbound
Decentralized Federated Learning by Partial Message Exchange Unresolved cited work
Reference 58
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Observation a8fd5d4a-8a5f-4608-96dd-0ac4df236f9e · outbound
Decentralized Federated Learning by Partial Message Exchange Proof.1) By the triangle inequality and Cauchy-Schwarz: ∥Ewk+1 −Ew k∥ ≤E∥wk+1 −w k∥ ≤ q E∥wk+1 −w k∥2 ≤ c γk/2
Reference 59
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Observation 1c0c8556-5887-4ea9-b2c0-f5e879c438f7 · outbound
Decentralized Federated Learning by Partial Message Exchange This implies that there is a constantc >0such that |f(w k)−f(w ∞)| ≤c∥wk −w ∞∥
Reference 60
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Observation a97cb2a7-caa5-4d6d-a14e-a5f0f747549f · outbound
Decentralized Federated Learning by Partial Message Exchange We first showlim N→∞ ∆N = 0
Reference 61
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Observation c65d6cd7-3f14-4568-bd50-8f784f7286f5 · outbound
Decentralized Federated Learning by Partial Message Exchange Unresolved cited work
Reference 62
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Observation 1ef97ead-80c7-46f7-badd-350516c3e562 · outbound
Decentralized Federated Learning by Partial Message Exchange Unresolved cited work
Reference 63
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Observation d9180cca-c463-4718-9698-2c356009f791 · outbound
Decentralized Federated Learning by Partial Message Exchange Proof.1) From (75) and (74), sequence{ eH k}is non-increasing and bounded from below
Reference 64
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Observation 24b0aa7e-2471-494c-a7c4-de3a5518fa6a · outbound
Decentralized Federated Learning by Partial Message Exchange It is easy to checkϖ k ∈N(2δ) for allk≥0
Reference 65
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Observation 88ae75f1-27bf-4814-bcb2-18ed97f10658 · outbound
Decentralized Federated Learning by Partial Message Exchange Unresolved cited work
Reference 66
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Observation 42b2c74a-bae9-4e82-bb26-2bd2f91b8fa4 · inbound
TallyTrain: Communication-Efficient Federated Distillation Decentralized Federated Learning by Partial Message Exchange
Reference 13
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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.