Paper Citation Record · LEDGER

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

As of 13 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 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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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
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External citation measurements

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.348596Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.409378Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.509695Z

Source-reported events for the cited work

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.270994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.394356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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-reported events for the cited work

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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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.547663Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.527511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.565017Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.576277Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.496917Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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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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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.592587Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.602833Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.608072Z

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Unavailable: canonical work link unavailable.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.482601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:42:06.618593Z

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

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

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:42:07.467718Z

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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

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:42:07.438411Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.423930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:42:07.379154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.363915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.333678Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.317741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.302349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.287428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.253696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.237399Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.220936Z

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

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

Reference 41

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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

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

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

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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

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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; raw_fallback, observed 2026-08-12T14:42:07.512264Z

Source-reported events for the cited work

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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

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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: verified fuzzy; raw_fallback, observed 2026-08-12T14:42:07.453245Z

Source-reported events for the cited work

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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

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Source-reported events for the cited work

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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; raw_fallback, observed 2026-08-12T14:42:07.542119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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

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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

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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

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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

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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

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Pith citing papers

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