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Paper Citation Record · LEDGER

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations

As of 16 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2411.15014.

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

pith.paper-citation-record.v1
2411.15014 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:42:06.718882Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

43 of 43 outbound references displayed

  • verified exact3
  • verified fuzzy19
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 038dc07f-54a5-4eb4-8151-5523b5efc04d · outbound

This paper cites ⟨ΦΦΦ∗ − ΦΦΦt, −1 N NX i=1 ¯h(θθθi t+1, ΦΦΦt)⟩ # | {z } Term 2 + 2βtE.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations ⟨ΦΦΦ∗ − ΦΦΦt, −1 N NX i=1 ¯h(θθθi t+1, ΦΦΦt)⟩ # | {z } Term 2 + 2βtE

Reference 1

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.664846Z digest=sha256:bea2f98e8523ce3bd0a450cf8330807de2bd247a869c9ff4af5a55aa8bb555e2

Observation 71d0d850-bf56-4f8a-bf95-56644536c38d · outbound

This paper cites When the state and action spaces are large, it is com- putationally infeasible to store Qi,πi (s, a) for all state-action pairs.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations When the state and action spaces are large, it is com- putationally infeasible to store Qi,πi (s, a) for all state-action pairs

Reference 2

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.644122Z digest=sha256:9150a408b5694ac9865d13f6fc726f28c29a141577a49745ca16010e8070a3fd

Observation ceba4d5b-f3d7-43cb-b6da-1bb1f6455a43 · outbound

This paper cites OpenAI Gym.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations OpenAI Gym

Reference 3

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source=pdf_text observed=2026-08-12T14:42:06.509695Z digest=sha256:4db07c5157d454b87f19ee98b47fa70ec450308d9039c6d7fe3b3ea2d248682c

Observation f8707061-5176-4fc1-969f-f81883379c16 · outbound

This paper cites (1 + βt−1/αt) (1 + 2βt−1/αt − 2αtKω ) + (12α2 t δ2K 2 + 2L2α3 t /βt−1 + 6K 2δ2α3 t /βt−1) 4β2 t−1L2 N ! + (1 + αt/βt−1) 4β2 t−1L4 N # · E h θθθi t − yi(ΦΦΦt−1) 2i +.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations (1 + βt−1/αt) (1 + 2βt−1/αt − 2αtKω ) + (12α2 t δ2K 2 + 2L2α3 t /βt−1 + 6K 2δ2α3 t /βt−1) 4β2 t−1L2 N ! + (1 + αt/βt−1) 4β2 t−1L4 N # · E h θθθi t − yi(ΦΦΦt−1) 2i +

Reference 5

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.690060Z digest=sha256:8b23b4a4a8a50da724f3d503b12bf8355d117258b21892e5a93fac76397f7594

Observation 3a1db86c-6920-4cf6-86ac-3b7d9461386c · outbound

This paper cites , Edo 2: Get the initial state of the environment; 3: for t = 0, 1, ..., T− 1 do 4: for i = 1,.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations , Edo 2: Get the initial state of the environment; 3: for t = 0, 1, ..., T− 1 do 4: for i = 1,

Reference 6

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source=pdf_text observed=2026-08-12T14:42:06.649263Z digest=sha256:7acc198b785a36b54bcc4173ac40e6e64fd30948d866e19df409014a178d5312

Observation fd616239-7398-408b-a104-c93c9faf8145 · outbound

This paper cites Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance

Reference 7

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source=pdf_text observed=2026-08-12T14:42:06.532131Z digest=sha256:dc6568cfaf5762952b734f794ae28d02dea02d5c9b8ad9a3d7659d161fcfdaac

Observation 07f71b43-d9f5-4e2c-9455-3c609b32da0f · outbound

This paper cites Federated Learning for Mobile Keyboard Prediction.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated Learning for Mobile Keyboard Prediction

Reference 10

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source=pdf_text observed=2026-08-12T14:42:06.547663Z digest=sha256:a88fd15611fd7f1dcb74150fca457139b869368a81a379c8aa60e0c88695bf7c

Observation b32fa44e-7949-4305-99b9-ac954710a977 · outbound

This paper cites Federated learning for resource-constrained IoT devices: Panoramas and state of the art.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated learning for resource-constrained IoT devices: Panoramas and state of the art

Reference 11

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source=pdf_text observed=2026-08-12T14:42:06.553883Z digest=sha256:3bb3c996b9d124cd068a4fa832f9e643ca33030d4536d44ee49da919306b829f

Observation f3e1da26-9abc-4a7b-9baa-d46e302e67e8 · outbound

This paper cites Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis

Reference 13

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source=pdf_text observed=2026-08-12T14:42:06.565017Z digest=sha256:4aa6b64f15ba576f892fb7dc1cae7a7626342cbc162e447d05133911e4c22217

Observation 4a672aa9-75a1-46b1-8b1b-df28aee56911 · outbound

This paper cites Model-free Representation Learning and Exploration in Low-rank MDPs.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Model-free Representation Learning and Exploration in Low-rank MDPs

Reference 15

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source=pdf_text observed=2026-08-12T14:42:06.576277Z digest=sha256:3c8288cc75bbd8cba505b3563f031b4c2aec1f3c1114e3087ff0ed4f3e95336a

Observation 32a20303-1ad8-4726-9920-b54a39269bb4 · outbound

This paper cites Federated reinforcement learning for fast personalization.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated reinforcement learning for fast personalization

Reference 16

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.582400Z digest=sha256:2b00ffef627411ecac1bd4d1851cf6cf9607c60717d7ea2f4bb0bb769f870072

Observation 79249f88-028b-44ea-992b-c43695326570 · outbound

This paper cites Federated Reinforcement Learning: Techniques, Applications, and Open Challenges.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 17

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source=pdf_text observed=2026-08-12T14:42:06.587289Z digest=sha256:74ed325332fa0de87d6d5c27ed93117e3dcf76ba8c3a4f0134d631e446bfd4f1

Observation a3c50314-475e-4a87-8374-c0265fb7d7eb · outbound

This paper cites The Sample-Communication Complexity Trade-off in Federated Q-Learning.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations The Sample-Communication Complexity Trade-off in Federated Q-Learning

Reference 18

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source=pdf_text observed=2026-08-12T14:42:06.592587Z digest=sha256:c0d648f8df9c460a60f7029d6bebbaf2ec80d1a1088d493cc0f915360984f01b

Observation e59e9cec-68e5-4d4a-bb13-ce7aaf9756d8 · outbound

This paper cites Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity

Reference 20

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source=pdf_text observed=2026-08-12T14:42:06.602833Z digest=sha256:1501028ff822127b19a9e05cd3d310a6e9270ee711d316900515463189a42350

Observation 82ce2e8b-5f5d-4461-8498-d0ceeb1a024f · outbound

This paper cites Applied Federated Learning: Improving Google Keyboard Query Suggestions.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Applied Federated Learning: Improving Google Keyboard Query Suggestions

Reference 21

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Observation ab6c4bf2-5af5-4e5e-8c3e-2102d4f6fbad · outbound

This paper cites Federated reinforcement learning for generalizable motion planning.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated reinforcement learning for generalizable motion planning

Reference 22

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.613391Z digest=sha256:9eed441b5e0c0b65e1c2223d0ea42ad7b209d2cae03bb73cfcc4399a3f8d54dc

Observation e177451b-2610-44a7-a933-52c490b80dbf · outbound

This paper cites Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning

Reference 23

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source=pdf_text observed=2026-08-12T14:42:06.618593Z digest=sha256:54fabee64e18f034cf0030b572209dcbacef661565db6f49c138c0f7dfba0617

Observation e00d9e6d-c828-4a13-a5d9-ff8accf7e0ce · outbound

This paper cites an unresolved cited work.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-12T14:42:06.623676Z digest=sha256:6c67632e82902f049f00d29e5202b4f50a114b44a6f483e7ac2d0695aa352dc3

Observation bae14689-f2d5-4027-abd7-1a2ad05424a9 · outbound

This paper cites an unresolved cited work.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Unresolved cited work

Reference 26

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source=pdf_text observed=2026-08-12T14:42:06.633945Z digest=sha256:117c667e5fa97f6709bcf95e4cba7a2724574934cf739ae79bae9e882cbec666

Observation 37cea18c-ae84-4282-abb7-00d2d7ec8c0f · outbound

This paper cites However, it is open in the context of leveraging representation learning in PFedFL.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations However, it is open in the context of leveraging representation learning in PFedFL

Reference 27

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source=pdf_text observed=2026-08-12T14:42:06.639153Z digest=sha256:18a96b0e5613ddeaf7b014dde91f003f4d1bb0d8ee128bf06c84a0e7a40c94b9

Observation 72b6cf77-f908-44e4-b574-61172557b490 · outbound

This paper cites an unresolved cited work.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Unresolved cited work

Reference 30

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source=pdf_text observed=2026-08-12T14:42:06.654598Z digest=sha256:2324364512a21e336c74cfbb6b960b99bf68d4f122830686f23ab0a2152dfb07

Observation 446b0021-fc88-4bd0-8c0d-85ba7f1c271d · outbound

This paper cites Hence, Li(ΦΦΦ(si k), θθθi) is convex on ΦΦΦ(si k) under a fixed θi.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Hence, Li(ΦΦΦ(si k), θθθi) is convex on ΦΦΦ(si k) under a fixed θi

Reference 31

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source=pdf_text observed=2026-08-12T14:42:06.659538Z digest=sha256:78cb4902ba3fe9209a2635526c8907ffa22062fa4bbbc3984354a7c93d2cc153

Observation 5abbb615-102e-4475-8bdd-dd7258bc6a0d · outbound

This paper cites NX i=1 ∥θθθi t+1 − yi(ΦΦΦt)∥2 # . (29) Proof. We have Term 2 = 2βtE.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations NX i=1 ∥θθθi t+1 − yi(ΦΦΦt)∥2 # . (29) Proof. We have Term 2 = 2βtE

Reference 33

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source=pdf_text observed=2026-08-12T14:42:06.669864Z digest=sha256:77b9b2340d181c34df33ba23d95169ecb60c8c20cee97e0c597fcb697a8f4c8d

Observation 696f500d-06f4-48cf-9a25-d971d77573e3 · outbound

This paper cites The proof is similar to that of Lemma 3 in Dal Fabbro et al.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations The proof is similar to that of Lemma 3 in Dal Fabbro et al

Reference 34

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source=pdf_text observed=2026-08-12T14:42:06.674880Z digest=sha256:c65c7f4d784b70ef1bd1aa9cc9c1dc6d36a8f2c0d36485b8ba1dd66e1c92a0b1

Observation 35dab6f8-e9cc-47ea-9d24-85e61e954513 · outbound

This paper cites 29 Published as a conference paper at ICLR 2025 Proof.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations 29 Published as a conference paper at ICLR 2025 Proof

Reference 35

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source=pdf_text observed=2026-08-12T14:42:06.680252Z digest=sha256:f4daffc00d45fe72fe73fa53e5fc1737dbe3ff2fd5c0505f62b913432bfff3e4

Observation bdf2a534-246b-436f-9b49-4665a4440aaa · outbound

This paper cites * θθθi t − yi(ΦΦΦt−1), KX k=1 g(θθθi t,k−1, ΦΦΦt) +# ≤ E h θθθi t − yi(ΦΦΦt−1) 2i + 6α2 t δ2K 2E h ∥ΦΦΦt − ΦΦΦ∗∥2 i + 6α2 t δ2K 2(1 + B2) + 2α2 t K 2L2B2 + 2αtE.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations * θθθi t − yi(ΦΦΦt−1), KX k=1 g(θθθi t,k−1, ΦΦΦt) +# ≤ E h θθθi t − yi(ΦΦΦt−1) 2i + 6α2 t δ2K 2E h ∥ΦΦΦt − ΦΦΦ∗∥2 i + 6α2 t δ2K 2(1 + B2) + 2α2 t K 2L2B2 + 2αtE

Reference 36

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.684976Z digest=sha256:814b42ac6b5dc9a85ed2861bce179341770c22aab4f9b0469a009d6b3b0c96ff

Observation ddd0382f-d770-4115-8031-c91930343c91 · outbound

This paper cites (1 + βt/αt+1) (1 + 2βt/αt+1 − 2αt+1Kω ) + (12α2 t+1δ2K 2 + 2L2α3 t+1/βt + 6K 2δ2α3 t+1/βt) 4β2 t L2 N ! + (1 + αt+1/βt) 4β2 t L4 N # · 1 N E.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations (1 + βt/αt+1) (1 + 2βt/αt+1 − 2αt+1Kω ) + (12α2 t+1δ2K 2 + 2L2α3 t+1/βt + 6K 2δ2α3 t+1/βt) 4β2 t L2 N ! + (1 + αt+1/βt) 4β2 t L4 N # · 1 N E

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.694669Z digest=sha256:53a8802d8f98919c0e3845fe30cf1ddbe224260284fed89eb6fd83b85419ef17

Observation 207b5bae-a917-4a9f-a6c1-e44fcf6f43f6 · outbound

This paper cites E[∥ΦΦΦt − ΦΦΦ∗∥2] + 1 N E.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations E[∥ΦΦΦt − ΦΦΦ∗∥2] + 1 N E

Reference 39

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.699553Z digest=sha256:d91a754494c9349b5f1c67855e5b5bf21202bf948e24a5183599611f1353181f

Observation 16d09803-71ef-42a3-9ad7-86f75f00ddb7 · outbound

This paper cites PF EDDQN-R EP in Acrobot environment.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations PF EDDQN-R EP in Acrobot environment

Reference 40

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source=pdf_text observed=2026-08-12T14:42:06.704227Z digest=sha256:ab3ea66b613280e544f3c2373ccd956b1404724bdd9d5f057fbff18ac36e7c4f

Observation cfba1b4e-e0a6-4fc1-af64-09323c01044b · outbound

This paper cites an unresolved cited work.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Unresolved cited work

Reference 41

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source=pdf_text observed=2026-08-12T14:42:06.708860Z digest=sha256:cfa2113846ec24844f628b2d6525f5f98307d1d54853a30d62ea8fb86466417c

Observation 1c7b6026-a0ba-4003-bb92-9f28d5fc1c7e · outbound

This paper cites Figure 13: Worst case personalization error with varying pole length discrepancy across environ- ments.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Figure 13: Worst case personalization error with varying pole length discrepancy across environ- ments

Reference 42

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verified exact
raw_fallback, observed 2026-08-12T14:42:06.870467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 85d23234-0960-488e-a0bb-ed7dd0811986 · outbound

This paper cites an unresolved cited work.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Unresolved cited work

Reference 43

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7fa08076-28a6-4478-9b75-51af0140bd62 · outbound

This paper cites Personalized Federated Learning with Communication Compression.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Personalized Federated Learning with Communication Compression

Reference 2013

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b629bf20-7569-441d-8483-c4e465009569 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Asynchronous methods for deep reinforcement learning

Reference 2015

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 002eee82-2db3-490e-8239-7f49a69013a9 · outbound

This paper cites Federated Meta-Learning with Fast Convergence and Efficient Communication.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated Meta-Learning with Fast Convergence and Efficient Communication

Reference 2016

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f003c5de-0883-452a-9d4e-0ed99ebb4cb6 · outbound

This paper cites Federated reinforcement learning.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated reinforcement learning

Reference 2017

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7e609893-30bb-41a1-8138-b0da64aa5543 · outbound

This paper cites Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement Learning

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-12T14:42:06.521351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:42:06.521351Z digest=sha256:637bb02613812b07f75a2dc40c532cd1c20da117ee3f4457ef1b53981587f684

Observation 28c2266c-5516-403d-aba8-d84d932dafae · outbound

This paper cites Exploiting shared repre- sentations for personalized federated learning.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Exploiting shared repre- sentations for personalized federated learning

Reference 2019

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 36f5ea1a-2805-4d61-8769-fc821194a6c3 · outbound

This paper cites Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-12T14:42:06.537436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 866f3c36-2592-4d73-ba50-792967565713 · outbound

This paper cites Personalized Federated Learning: A Meta-Learning Approach.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Personalized Federated Learning: A Meta-Learning Approach

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-12T14:42:06.542443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8d8d1e64-7df7-4439-a032-c072af0c3c7a · outbound

This paper cites Private Learning with Public Features.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Private Learning with Public Features

Reference 2022

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:42:07.021393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:42:06.559832Z digest=sha256:3a47d92a54d0f15f419e356d659ba22fa7df370787882b5de6089ca862797088

Observation d633920a-3126-46cc-85fd-c8a45e64c57c · outbound

This paper cites Federated Learning with Personalization Layers.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Federated Learning with Personalization Layers

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-12T14:42:06.497266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:42:06.497266Z digest=sha256:584d09b4fada6bf0b151d5541d2f91605e8c4c279459eb53588e9fea3790499e

Observation e3667d99-aeb2-435b-9898-6659f46973b0 · outbound

This paper cites Proximal Policy Optimization Algorithms.

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations Proximal Policy Optimization Algorithms

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-12T14:42:06.597861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:42:06.597861Z digest=sha256:28d1d042ff4eb2456956494f9dfee43d78a3468b5cdcfdd7bdccdad7528c148e

Pith citing papers

No inbound Pith citation observations are available.