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Paper Citation Record · LEDGER

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity

As of 21 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2507.15601.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.15601 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:37:01.189240Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T15:24:04.079011Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-18T15:26:33.774099Z

Reference resolution

49 of 49 outbound references displayed

  • verified exact2
  • verified fuzzy35
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8dcb94b-6176-49e4-8fd5-1b38cab95f8a · outbound

This paper cites A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.387683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.914700Z digest=sha256:7d09a7f0c09bda2ff37f9ec01205b193b9eb45f152879f835c46b869d9dfa4a2

Observation 9cabefea-bf88-48c6-af11-107dce834990 · outbound

This paper cites Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G

Reference 2

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unresolved
no resolver link, observed 2026-08-06T15:37:00.920375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:00.920375Z digest=sha256:720221e830e1ea0746e9f38348cdfccdf91d51903a4ef867a5eb7a480ef2529f

Observation 498a4135-4849-4737-9270-f9c32238130e · outbound

This paper cites Space–ground fluid AI for 6G edge intelligence,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Space–ground fluid AI for 6G edge intelligence,

Reference 3

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raw_fallback, observed 2026-08-06T15:37:02.372930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.929395Z digest=sha256:207c9ce037e4a5ac063575c084f28e3e1a60955ac24ddb16c4213aad18a6556d

Observation c8cccb3b-c307-4cea-97aa-0c8081ea06a7 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Communication-efficient learning of deep networks from decentralized data,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.356360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.935843Z digest=sha256:986cfdb551da0399620e17ab511d9e38e1fed012536d9be5b414ec17500296e1

Observation 7a2debfd-8e32-42f9-8603-eb0251082c89 · outbound

This paper cites Federated machine learning: Concept and applications,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Federated machine learning: Concept and applications,

Reference 5

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no resolver link, observed 2026-08-06T15:37:00.941941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:00.941941Z digest=sha256:0045858957add3ec5cd6ca80683721114787e0e571bbef90003852034c537b33

Observation 353a3a87-7351-419c-99db-ee68d50fd2dd · outbound

This paper cites Advances and open problems in federated learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Advances and open problems in federated learning,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.330975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.949478Z digest=sha256:c75142da1f72e7dc4706db68294676c7fac0a6bcfc24cd9bc502a0baf31ad446

Observation 7804f621-a3b6-4113-b99c-b9a0683be050 · outbound

This paper cites Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.314298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.955106Z digest=sha256:8498ed2fe0a087752c12bff45a029c383e9e04d7e68c80c4d2f0c0f987a178cd

Observation ee05dba4-ee19-4c47-9041-90ba5f0527c0 · outbound

This paper cites GeFL: Gradient encryption- aided privacy preserved federated learning for autonomous vehicles,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity GeFL: Gradient encryption- aided privacy preserved federated learning for autonomous vehicles,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.298949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.962686Z digest=sha256:37be87a2f02a9d38cb2e24c5ad0337ba68bdb5da39e66dcb22127a1426641619

Observation 42002947-c4fd-49ce-bd82-6a2b99bbf5a1 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Federated Learning: Strategies for Improving Communication Efficiency

Reference 9

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unresolved
no resolver link, observed 2026-08-06T15:37:00.967659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:00.967659Z digest=sha256:0b571c60d107c59407b59701671d0ca69742111be7950075ad7aa57a18072307

Observation 7316513a-b46a-4541-928c-93c17e83b864 · outbound

This paper cites Model pruning enables efficient federated learning on edge devices,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Model pruning enables efficient federated learning on edge devices,

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.283901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.972864Z digest=sha256:1a4875d2ee35811c70f43ccee15b3967c8881eff58062cdebb848673148a6c9b

Observation 23ca3f55-0c6e-4fe5-b358-5d3372cf3a4e · outbound

This paper cites Deploying federated learning in large-scale cellular networks: Spatial convergence analysis,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Deploying federated learning in large-scale cellular networks: Spatial convergence analysis,

Reference 11

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.268906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.978036Z digest=sha256:ed67570b5ecacefd3e374ad6f9573162fc52f2facd97c2be8f3667ff5095653b

Observation 14f4e6db-155d-4301-bfea-de9e28571a0c · outbound

This paper cites UVeQFed: Universal vector quantization for federated learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity UVeQFed: Universal vector quantization for federated learning,

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.254445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.983173Z digest=sha256:1d7f0ec0d0f7c9ed7eacb627b36ca9d38df7428b2de416a57439e9d053e682b9

Observation 643f8d89-45e3-469a-a11b-4bac4179648c · outbound

This paper cites Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression

Reference 13

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local_arxiv, observed 2026-08-06T15:37:01.641832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.987955Z digest=sha256:9dbc1ffb1a4ad02752457e17cb713946f72f1a645730abf19478b76fa2eaad23

Observation b87be946-3699-41e0-88a2-3bf53d95cca4 · outbound

This paper cites Splitfed: When federated learning meets split learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Splitfed: When federated learning meets split learning,

Reference 14

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unresolved
no resolver link, observed 2026-08-06T15:37:00.994277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:00.994277Z digest=sha256:3545c580d920c97ad8aa11fae357cd2aa67be3a17db2aa1c794cd30cd3f34960

Observation 520cbb0d-d066-4cfe-92ba-88feda161fee · outbound

This paper cites Broadband analog aggregation for low-latency federated edge learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Broadband analog aggregation for low-latency federated edge learning,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.229138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:00.999196Z digest=sha256:a686bd8cb61dbf3b69d0e6506298d37290798f67a5ff452adbecc85b199c5fc7

Observation 256c6687-38c7-49d3-85ff-02037b01ba0d · outbound

This paper cites Federated learning via over- the-air computation,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Federated learning via over- the-air computation,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.215352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.007857Z digest=sha256:ef482c440955bbbe3cffc16c90c8ef1d0bf385234c6446e4c2c4e4d390198f4a

Observation b56b1464-ce1a-4d39-9158-79d24ec68518 · outbound

This paper cites Spectrum breathing: Protecting over-the-air federated learning against interference,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Spectrum breathing: Protecting over-the-air federated learning against interference,

Reference 17

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raw_fallback, observed 2026-08-06T15:37:02.199963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.013355Z digest=sha256:f13cbd51b7945229885f235c7c0e2addffc2b353583dd6a6876fc26fc95a6236

Observation ddf4775f-0d03-48ee-a573-a9581e925782 · outbound

This paper cites Airbreath sensing: Protecting over-the-air distributed sensing against interference,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Airbreath sensing: Protecting over-the-air distributed sensing against interference,

Reference 18

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no resolver link, observed 2026-08-06T15:37:01.018242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:01.018242Z digest=sha256:879db9b90e8cf62e4d7bd62add7f4fccd79baabd6a6eb3f3f816862e1c6df991

Observation 8d02eb3f-1305-4fc7-b705-7603c7ac9d1c · outbound

This paper cites Federated learning over wireless fading channels,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Federated learning over wireless fading channels,

Reference 19

Resolution
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raw_fallback, observed 2026-08-06T15:37:02.184339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.024023Z digest=sha256:9e77a3af181ecbd5944a3b05ea1fab5534d9514d05a6427b720226d159a7ab46

Observation 13cba22f-359a-4214-8435-9befa4382cb2 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 20

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no resolver link, observed 2026-08-06T15:37:01.029636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:01.029636Z digest=sha256:9d4f2d0cfd246c652cc2dc4c56cb6600e4e1f9eccb61b1249fb474091f2c47f2

Observation 0b646557-2f4e-46f5-85b6-35e92d26514d · outbound

This paper cites To talk or to work: Dynamic batch sizes assisted time efficient federated learning over future mobile edge devices,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity To talk or to work: Dynamic batch sizes assisted time efficient federated learning over future mobile edge devices,

Reference 21

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raw_fallback, observed 2026-08-06T15:37:02.166172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.035876Z digest=sha256:452f9df64b0602a7f098ab32c16cad8f8d5056f4bf9c9986ec3e1c47ec797ed9

Observation 95f586ac-404e-4741-8d95-46c4d8caa8d9 · outbound

This paper cites ARM Cortex-M7 Processor Datasheet,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity ARM Cortex-M7 Processor Datasheet,

Reference 22

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raw_fallback, observed 2026-08-06T15:37:02.149470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.041445Z digest=sha256:54df3a4b6e150070710ce12203d77635ead0bf73f3917c6e43174b39615b814b

Observation efec4618-3dd3-49c5-a1c1-ef2d0cffb553 · outbound

This paper cites Apple A18 Pro Chip Specifications,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Apple A18 Pro Chip Specifications,

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.131528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.046432Z digest=sha256:b845904f76e9ac4cca0e268d8bb1d6770834ab319304c52e0772df3f1fc94794

Observation 0ca72c45-a87b-4f0e-8314-4d845f85de04 · outbound

This paper cites Asynchronous Federated Optimization.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Asynchronous Federated Optimization

Reference 24

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no resolver link, observed 2026-08-06T15:37:01.051178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:01.051178Z digest=sha256:51d3f46fe5dedb81136305940ffc735dfa2b013f580d1af61b4e0e6c0d4aea03

Observation a3aa833a-7a25-4f85-aed1-0d3a3890abb5 · outbound

This paper cites Asynchronous federated learning over wireless communication networks,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Asynchronous federated learning over wireless communication networks,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.103001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.057160Z digest=sha256:c3857c91fc2f2f71f50e70ebb1fc8c0acb8a75538035bc7a53d62fd233e48ca5

Observation 26ca3aca-d19a-4896-9643-96a6009e38bc · outbound

This paper cites Asynchronous federated learning on heterogeneous devices: A survey,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Asynchronous federated learning on heterogeneous devices: A survey,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.083582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.061927Z digest=sha256:94e8c3b1a8f994f6beda30b5df1016bdb659c21faa58d70ce0590560981ee92c

Observation 334d32d9-8901-44ac-967a-5e338c80dbe9 · outbound

This paper cites Revisiting Distributed Synchronous SGD.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Revisiting Distributed Synchronous SGD

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T15:37:01.067056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:01.067056Z digest=sha256:74150fc076cc07bcf91f296ac2fa9657a42598b8f53667268fac367936e03c3c

Observation 80f15f1f-8c58-4b4c-9879-a8db5137a6ee · outbound

This paper cites Bandwidth allocation for multiple federated learning services in wireless edge networks,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Bandwidth allocation for multiple federated learning services in wireless edge networks,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.061378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.073703Z digest=sha256:5a130b5e2e3256c519d4ab2e5c6961da33ae8549665eae7e92b55d0b49cb3d67

Observation 234950dc-ef25-45dc-9ef0-a160114cc7a7 · outbound

This paper cites Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.044580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.079851Z digest=sha256:048753e98c6fc78646c5bfc28219834b8d5760d8af910cbd60addb4cc77b2384

Observation 37dbb8db-67f1-4711-8ac4-9d6466e15797 · outbound

This paper cites Joint device schedul- ing and resource allocation for latency constrained wireless federated learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Joint device schedul- ing and resource allocation for latency constrained wireless federated learning,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.026358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.084400Z digest=sha256:358f142cb2aaef50e860cb428ec881e25d6a3e0b11f50f8fa68d29e451999486

Observation f40c894f-96e0-4668-aefe-0d2dc5fb39e9 · outbound

This paper cites Wirelessly powered federated edge learning: Optimal tradeoffs between convergence and power transfer,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Wirelessly powered federated edge learning: Optimal tradeoffs between convergence and power transfer,

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:02.007007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.090296Z digest=sha256:680bc0aaaa58b01da60e603808d4e3f2f219b760219e8f49879d48a9b6752487

Observation f12bd5fe-d6ed-4d45-b9dc-b5e6c3a8f96a · outbound

This paper cites Adaptive batch size for federated learning in resource-constrained edge computing,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Adaptive batch size for federated learning in resource-constrained edge computing,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.987654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.095112Z digest=sha256:fd4135b71b20415ce1618ff878a135a4c15ebc39c48330bf500519aad692d8e8

Observation 31f09d37-217f-4d6e-b29a-6bae404c6465 · outbound

This paper cites AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices,

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.963176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.104145Z digest=sha256:36222d2b829f249fc2d23796d34774d045957e9d0e433c977ed3466fd8280388

Observation 52852d83-611c-4cfa-8634-415a8d04a318 · outbound

This paper cites Optimal batch allocation for wireless federated learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Optimal batch allocation for wireless federated learning,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.944062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.110360Z digest=sha256:cf8fdee01a9ea0297aa1916ac10b9beada247e91fe5a514bccc769ca46cd251e

Observation bcd4bf94-6cff-4eba-8045-ba7c8d010051 · outbound

This paper cites Accelerating DNN training in wireless federated edge learning systems,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Accelerating DNN training in wireless federated edge learning systems,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.924433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.116111Z digest=sha256:bd9cdee038a79453f99c10213b64f1620259fa70e2fd9d3b4e5fa19fcf44966b

Observation 17772427-4c5b-47f2-9431-a6437275b40a · outbound

This paper cites Adaptive batchsize selection and gradient compression for wireless federated learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Adaptive batchsize selection and gradient compression for wireless federated learning,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.907488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.121906Z digest=sha256:e005d9495fd8b9b3da4924df6d6f1f9466d57a9c58b424de3ed0f50d3fc089cd

Observation 96e0fce2-d3a8-498f-baac-19dee4716782 · outbound

This paper cites DYNAMITE: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming datasets,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity DYNAMITE: Dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming datasets,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.888296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.127978Z digest=sha256:5b52055f82dbc4c1037094f97a3fc72963d633310de8e04c05d05a4a259d4c57

Observation 03248b12-57f2-4bb7-8be2-3a3a0c57b694 · outbound

This paper cites On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization

Reference 38

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:37:01.451148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.133527Z digest=sha256:05e6be836c30e52e4a48d755765f6849a6bbd8db46f6135c7329bb9d6f4e7d2e

Observation bcc73cc1-c011-4380-abfb-5ed1d73bd75a · outbound

This paper cites Parallel restarted SGD with faster con- vergence and less communication: Demystifying why model averaging works for deep learning,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Parallel restarted SGD with faster con- vergence and less communication: Demystifying why model averaging works for deep learning,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.867539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.138639Z digest=sha256:524c7031e81e498c490857f5a100536ff0ce96c49943cc8b218d4fb072ef4da7

Observation 8ceca58d-6546-4941-8c1d-4bf53fa80b42 · outbound

This paper cites One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.848904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.143287Z digest=sha256:1ae705752e7ce40b98625c23a8cb821a9e525f50481778209149bed7daa3706c

Observation 3ba11c7a-060c-4bfc-98b7-a12bede51f12 · outbound

This paper cites A method for the solution of certain non-linear problems in least squares,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity A method for the solution of certain non-linear problems in least squares,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.828437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.149458Z digest=sha256:e5555907176f611f5d36eb3f1d2257e8829bb8f8ddf34f9a6d505c6a0bfbb913

Observation 35267146-32a4-40d0-9aa0-211ff634e27a · outbound

This paper cites an unresolved cited work.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-06T15:37:01.796085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.154774Z digest=sha256:d6058b2a5d5d2c0132fe3c6d652ac04f67bfa43e71a8a02c8ce96c5eb5478720

Observation a08dc60b-0623-4f82-9f19-a6eebe39971e · outbound

This paper cites Ultra-Low-Latency Edge Inference for Distributed Sensing.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Ultra-Low-Latency Edge Inference for Distributed Sensing

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T15:37:01.159038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:01.159038Z digest=sha256:24f1e518855f93af99b5e01201df36a58c5e86db942211074d7654f46e67bfc6

Observation 369b842a-7cd4-4bde-aa8b-15eaa9d332ba · outbound

This paper cites Accessed: Oct.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Accessed: Oct

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.775272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.164377Z digest=sha256:4363f9e3ab94f167d2fd8608313902a600d8f9692e11fa153b392514ff293b9c

Observation 27152bde-f955-4963-8141-f277170a1c35 · outbound

This paper cites Accessed: Oct.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Accessed: Oct

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.757417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.169269Z digest=sha256:02c605e7ee42fd26a0f52b3eaa85ea51711190f130de4968fe51df896935cf98

Observation 6886ecac-b194-4714-b23c-98da5447d332 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Gradient-based learning applied to document recognition,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.740111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.173898Z digest=sha256:fb2a1e507d6bc4ae5f500dcd48012c1d23a487f75476441341b02a098db9c4c6

Observation e7fc5793-0144-41a9-88b1-ccf635c80bd8 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Learning multiple layers of features from tiny images,

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T15:37:01.179690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:01.179690Z digest=sha256:ee04a0d8fbfffe1360b9d453c1da69d285728d66fec573fe978ed6ea0eaed44c

Observation 37c2732d-6fc6-4b14-8db2-0fccb598c518 · outbound

This paper cites Deep residual learning for image recognition,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Deep residual learning for image recognition,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:37:01.711126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:37:01.184782Z digest=sha256:3bb287e1308ed31ccf57f682a049be8750757af80acf3a42ec42b8f7bfb4059a

Observation 3fca37bc-2096-4fc7-9a95-20d6a1d84b6f · outbound

This paper cites Revisiting outage for edge inference systems,.

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity Revisiting outage for edge inference systems,

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-06T15:37:01.189240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:37:01.189240Z digest=sha256:d431e63025f8b1037f668f4f5dd73d960b2584f46e77211660d6a12dfaddcae5

Pith citing papers

Observation b85938c2-440d-4ebc-a1dd-467dd5a1bb8e · inbound

Optimizing Split Federated Learning with Unstable Client Participation cites this paper.

Optimizing Split Federated Learning with Unstable Client Participation Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-18T15:26:33.777262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-18T15:24:04.079011Z digest=sha256:ccfe4448a51efa5df77566a9d22ec8a2e307c72f44cdba4f33fec2c51d66bedb