Pith. sign in

Paper Citation Record · LEDGER

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks

As of 18 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2412.06414.

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

pith.paper-citation-record.v1
2412.06414 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:45:32.388062Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy10
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6a71975-b902-42e9-b8f1-a2656c16dd5a · outbound

This paper cites Federated learning over wireless networks: Con- vergence analysis and resource allocation,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Federated learning over wireless networks: Con- vergence analysis and resource allocation,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.513254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.340254Z digest=sha256:7d2d218f83b223c0e39c45e8b877e8306b347422d7060c95b07f437c60b5e6aa

Observation 3902b38d-dbc3-43c0-9e12-a9c12a7856ac · outbound

This paper cites FedSL: Federated split learning for collaborative healthcare analytics on resource-constrained wearable iomt devices,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks FedSL: Federated split learning for collaborative healthcare analytics on resource-constrained wearable iomt devices,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.505438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.350564Z digest=sha256:ba6f84b33f641ece7203a76fd9478075c484ec7a7bbde3c560879c6ea5d22bbf

Observation ab5a296c-5497-4699-8d44-068b5d61eca4 · outbound

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

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks SplitFed: When federated learning meets split learning,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.497350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.353700Z digest=sha256:834ad04b5f333143a57ff2b657b2759ed86bea1d35d2c37c3093fec6ccc58b2f

Observation b617e984-5112-4bfc-a010-dda2dca23c6c · outbound

This paper cites Efficient parallel split learning over resource-constrained wireless edge networks,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Efficient parallel split learning over resource-constrained wireless edge networks,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.489912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.356505Z digest=sha256:00dbe05080ae42b657047b5f572e23e196c4fc01905a1d23da690821b4698000

Observation 2c0e4812-82be-4f5c-b780-53b43149f920 · outbound

This paper cites Accelerating split federated learning over wireless com- munication networks,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Accelerating split federated learning over wireless com- munication networks,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.481995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.365606Z digest=sha256:26a1382d3644b7994689ebc79693258595be33227548cc28c752d3d069630477

Observation 4db19207-beaa-42c2-bb82-b50df3e36b05 · outbound

This paper cites AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T19:45:32.368436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:45:32.368436Z digest=sha256:f8c923651f180c8e59130b58c1d9c14fd35d299a2d1fdff2b75f71e23b002582

Observation 930ecf08-b713-4e2b-a244-9362214dab61 · outbound

This paper cites Importance estimation for neural network pruning,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Importance estimation for neural network pruning,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.473557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.371748Z digest=sha256:488406182b2fc1805de4384be438f8591021b60ff9d0a61fa9acd5856f72e4be

Observation 0ff89d80-3881-44d9-ada3-814d509f0745 · outbound

This paper cites PLATON: Pruning large transformer models with upper confidence bound of weight importance,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks PLATON: Pruning large transformer models with upper confidence bound of weight importance,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.465544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.374187Z digest=sha256:0cb68b41eee075fdf524e279ae3874e45f5c2075ce86c6cfd44d9e470f9cfc38

Observation ab1ce143-6283-4e29-884d-231f5e02e68e · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T19:45:32.377002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:45:32.377002Z digest=sha256:cc930735164f2f8ac2f788af52cd5f89ac0e1b317c204083e4a6f42c5b352d9b

Observation 40494976-0b6d-4d06-a586-5131b9fc25a3 · outbound

This paper cites Federated Learning With Quantized Global Model Updates.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Federated Learning With Quantized Global Model Updates

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T19:45:32.380255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:45:32.380255Z digest=sha256:06d5a2538dccac03ed1c2ab20df663a62b53e2fe9e1e8cdb138467d1d369f4fc

Observation 35f13aee-c002-4266-8a95-5a202317c089 · outbound

This paper cites FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.457391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.383230Z digest=sha256:f3c9fb0c6655dd8d147b2d79f8cfa6777f3b17ce3bc16fe8c98a70245846167b

Observation ded165ba-ebc2-4f00-a59f-0168614d47b4 · outbound

This paper cites Sparsified SGD with memory,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Sparsified SGD with memory,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.449197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.385653Z digest=sha256:91c547df64216b4ba3769d13850693614902f7f08338a6e05c3324489de898ff

Observation bc2e08d9-78e1-4a80-9f99-bea6f9a24eed · outbound

This paper cites Quantized federated learning under transmission delay and outage constraints,.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Quantized federated learning under transmission delay and outage constraints,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T19:45:32.441104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T19:45:32.388062Z digest=sha256:8ab5fc5c5f94d1fa7e33119d20651044198fd2a5fce57951f3aac5fb1cdf98bd

Observation d5f68fa6-27dc-4ab3-bcd5-3ec949d17034 · outbound

This paper cites Split learning for health: Distributed deep learning without sharing raw patient data.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Split learning for health: Distributed deep learning without sharing raw patient data

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-11T19:45:32.347563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:45:32.347563Z digest=sha256:2c1d0b059434145ace13196c1f7759c3119c2145885bec96f421d2c7c5606465

Observation b4a498e9-8335-4a7f-a98a-1677841edce3 · outbound

This paper cites Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning.

Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-11T19:45:32.363143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:45:32.363143Z digest=sha256:8e3d32c4b8976c5b7b0876299133c9eb6415ea9c1187a0bfb1ec5b8b5cda5689

Pith citing papers

No inbound Pith citation observations are available.