Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:19:04.715741Z
Paper Citation Record · LEDGER
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions
As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.02897.
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 2026-08-10T06:31:04.303077+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
20 of 20 outbound references displayed
- verified exact2
- verified fuzzy7
- unresolved11
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c4c999bd-0480-4214-8a48-fa3cfe4ee1b9 · outbound
This paper cites Due to Assumption 1, if the loss is L-smooth, we can apply the Descent Lemma, from Chapter 2, Theorem 2.1.5 in Nesterov (2014).
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Due to Assumption 1, if the loss is L-smooth, we can apply the Descent Lemma, from Chapter 2, Theorem 2.1.5 in Nesterov (2014)
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:19:06.324370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e4c13a73-fcb5-43cf-bba9-dc825317a819 · outbound
This paper cites Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:03.261637Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 22fafe38-fd00-403c-857f-0854a0f5649a · outbound
This paper cites Quality-aware client selection and resource optimization for federated learning in computing networks.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Quality-aware client selection and resource optimization for federated learning in computing networks
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:19:07.442513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2dca3283-116b-470d-bf10-fb12a70b6579 · outbound
This paper cites Social network community detection using agglomerative spectral clustering.Complexity, 2017:1–10, 11.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Social network community detection using agglomerative spectral clustering.Complexity, 2017:1–10, 11
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:19:07.243340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 1cfef5f3-1499-4c41-ba82-988a71faa00f · outbound
This paper cites Federated Learning with Non-IID Data.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Federated Learning with Non-IID Data
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:04.002394Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ccc02a43-c87d-4341-a131-4275576a3d70 · outbound
This paper cites A NOTATION For readability, we summarize below the notation conventions adopted throughout the paper.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions A NOTATION For readability, we summarize below the notation conventions adopted throughout the paper
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:19:06.764245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b872347a-f7b1-4c65-bade-b54d31d6bedd · outbound
This paper cites Given now that C d is positive definite since it is an invertible covariance matrix, then from Sylvester Criterion also its submatrixC d AA is invertible and the thesis follows.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Given now that C d is positive definite since it is an invertible covariance matrix, then from Sylvester Criterion also its submatrixC d AA is invertible and the thesis follows
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:19:06.527750Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9f0e8529-33d2-45d0-8b5d-d22de6552b24 · outbound
This paper cites an unresolved cited work.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T11:19:06.089945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2fced3d0-28c1-4db2-80df-29935ee67cf0 · outbound
This paper cites While the distinctive server-side operations of FedCVR-Bolt , such as clustering based on individual models {θk(t)} and deriving ¯θk(t+.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions While the distinctive server-side operations of FedCVR-Bolt , such as clustering based on individual models {θk(t)} and deriving ¯θk(t+
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:19:05.857669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 5cd3c937-a817-4644-a739-13531cd08ecb · outbound
This paper cites an unresolved cited work.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T11:19:05.641546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation eb2c9333-638a-4140-99a3-b8b3423266ae · outbound
This paper cites FedMD: Heterogenous Federated Learning via Model Distillation.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions FedMD: Heterogenous Federated Learning via Model Distillation
Reference 1998
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:03.359170Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8f549f15-1239-46a1-9ef0-504c2957d473 · outbound
This paper cites A Game-Theoretic Approach to Distributed Coalition Formation in Energy-Aware Cloud Federations (Extended Version).
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions A Game-Theoretic Approach to Distributed Coalition Formation in Energy-Aware Cloud Federations (Extended Version)
Reference 2001
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T11:19:05.390740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c7375b57-5aa1-4aee-88ea-8a730eb66ae5 · outbound
This paper cites Federated Learning with Matched Averaging.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Federated Learning with Matched Averaging
Reference 2007
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:03.945425Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dff28bfc-0754-4781-9e92-cb8c3afee3f5 · outbound
This paper cites Masato Ota, Yuko Sakurai, and Satoshi Oyama.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Masato Ota, Yuko Sakurai, and Satoshi Oyama
Reference 2014
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T11:19:07.034670Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 99c72ace-7f57-4612-985d-160493520b86 · outbound
This paper cites HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:02.890635Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca160a99-dcf7-4d15-8d7d-0a6cf923b8d1 · outbound
This paper cites LEAF: A Benchmark for Federated Settings.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions LEAF: A Benchmark for Federated Settings
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:02.797411Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b9c0911-0863-4706-8b01-5be5f784f5b8 · outbound
This paper cites Active Federated Learning.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Active Federated Learning
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:03.006757Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d558b236-8c47-490c-b832-4625f060a396 · outbound
This paper cites Practical Secure Aggregation for Federated Learning on User-Held Data.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Practical Secure Aggregation for Federated Learning on User-Held Data
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:02.734779Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 44b4773e-b50c-4158-94e2-a17578792117 · outbound
This paper cites 22nd IFAC World Congress.
Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions 22nd IFAC World Congress
Reference 2023
- Resolution
- verified exact
- raw_fallback, observed 2026-08-07T11:19:05.062885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 570c627e-4fc9-4f68-a091-37413a2e07bf · outbound
This paper cites Noah Mark.
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:19:03.593792Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.