Paper Citation Record · LEDGER

Many-Task Federated Fine-Tuning via Unified Task Vectors

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2502.06376.

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

pith.paper-citation-record.v1
2502.06376 v3

Coverage vector

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-08-10T19:34:52.061222Z

Reference resolution

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External citation measurements

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Outbound references

Observation c8fad235-5c1b-41d9-a25a-e9abbc623d29 · outbound

This paper cites Natural gradient works efficiently in learning.

Many-Task Federated Fine-Tuning via Unified Task Vectors Natural gradient works efficiently in learning

Reference 1

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Observation 2d111538-c8dc-48ab-bf1e-87afc104b98f · outbound

This paper cites FedBone: Towards Large-Scale Federated Multi-Task Learning.

Many-Task Federated Fine-Tuning via Unified Task Vectors FedBone: Towards Large-Scale Federated Multi-Task Learning

Reference 7

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Observation 1e273f64-f1a0-4fb3-a404-6a472dfe0d4b · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Many-Task Federated Fine-Tuning via Unified Task Vectors Remote sensing image scene classification: Benchmark and state of the art

Reference 8

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Observation 040d7bde-8f7d-42f4-872f-f3b3582794cf · outbound

This paper cites Cimpoi, S.

Many-Task Federated Fine-Tuning via Unified Task Vectors Cimpoi, S

Reference 10

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Observation 3a1470e5-0c80-4826-8db7-b000df79dfab · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Many-Task Federated Fine-Tuning via Unified Task Vectors An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 13

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Observation f34c293c-4160-4626-8c42-81b5a674533c · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,

Reference 15

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Observation 425c669a-e3dc-4d54-8f7e-2a686429388d · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Many-Task Federated Fine-Tuning via Unified Task Vectors Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 16

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Observation 63bfdde1-c420-48d1-a2fc-0bb826570d63 · outbound

This paper cites Emr-merging: Tuning-free high-performance model merging,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Emr-merging: Tuning-free high-performance model merging,

Reference 17

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Observation 33fde33c-27ba-4fcc-8386-2875b554e3f2 · outbound

This paper cites 3d object representations for fine-grained cate- gorization.

Many-Task Federated Fine-Tuning via Unified Task Vectors 3d object representations for fine-grained cate- gorization

Reference 19

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Observation 51864d6c-f733-44b3-8c3e-e6e2ee2f7277 · outbound

This paper cites Mnist handwritten digit database.

Many-Task Federated Fine-Tuning via Unified Task Vectors Mnist handwritten digit database

Reference 21

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Observation 7146c2fe-1519-492c-be62-3a8b2034bff9 · outbound

This paper cites Federated learning on non-iid data silos: An experimental study,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Federated learning on non-iid data silos: An experimental study,

Reference 23

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Observation d1575371-19ab-4ad7-97c8-2335897e9c87 · outbound

This paper cites Wasserstein task embedding for measur- ing task similarities.

Many-Task Federated Fine-Tuning via Unified Task Vectors Wasserstein task embedding for measur- ing task similarities

Reference 24

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This paper cites Communication-efficient learning of deep networks from decen- tralized data.

Many-Task Federated Fine-Tuning via Unified Task Vectors Communication-efficient learning of deep networks from decen- tralized data

Reference 26

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Observation dbfd61ec-f8fb-4b8b-957b-95a9f49d0530 · outbound

This paper cites Fed up with complexity: Simplifying many-task federated learning with NTKFedavg.

Many-Task Federated Fine-Tuning via Unified Task Vectors Fed up with complexity: Simplifying many-task federated learning with NTKFedavg

Reference 27

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Observation 0d0eb8b7-55a0-4369-a8ca-744189058750 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Many-Task Federated Fine-Tuning via Unified Task Vectors Reading digits in natural images with unsupervised feature learning

Reference 28

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Observation 184c1e3e-2e43-4320-88bf-0f9aae997d82 · outbound

This paper cites Task arithmetic in the tangent space: Improved editing of pre-trained models,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Task arithmetic in the tangent space: Improved editing of pre-trained models,

Reference 30

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Observation ad42d544-3981-4767-b31f-e5ad0b236c48 · outbound

This paper cites FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering.

Many-Task Federated Fine-Tuning via Unified Task Vectors FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering

Reference 31

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This paper cites The german traffic sign recognition benchmark: A multi-class classification competition.

Many-Task Federated Fine-Tuning via Unified Task Vectors The german traffic sign recognition benchmark: A multi-class classification competition

Reference 32

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Observation ed2f0f96-f911-4d2d-a8e4-df8f36650d8a · outbound

This paper cites Improving lora in privacy-preserving federated learning,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Improving lora in privacy-preserving federated learning,

Reference 33

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This paper cites Gradient masked averaging for federated learning,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Gradient masked averaging for federated learning,

Reference 34

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Many-Task Federated Fine-Tuning via Unified Task Vectors Federated fine-tuning of foundation models via probabilistic masking,

Reference 35

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Observation e97defab-cf7c-423f-98db-7c47efd91b45 · outbound

This paper cites Spot: Better frozen model adaptation through soft prompt transfer,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Spot: Better frozen model adaptation through soft prompt transfer,

Reference 36

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Many-Task Federated Fine-Tuning via Unified Task Vectors Unresolved cited work

Reference 37

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This paper cites Adamerg- ing: Adaptive model merging for multi-task learning,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Adamerg- ing: Adaptive model merging for multi-task learning,

Reference 38

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This paper cites pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning.

Many-Task Federated Fine-Tuning via Unified Task Vectors pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

Reference 39

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This paper cites Language models are super mario: Absorbing abili- ties from homologous models as a free lunch,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Language models are super mario: Absorbing abili- ties from homologous models as a free lunch,

Reference 40

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This paper cites Taskon- omy: Disentangling task transfer learning,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Taskon- omy: Disentangling task transfer learning,

Reference 41

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This paper cites Federated learning with personalization layers,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Federated learning with personalization layers,

Reference 1998

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Observation 7bf662ff-bb84-4756-a558-8eff24a4d40f · outbound

This paper cites Pactran: Pac-bayesian metrics for estimating the transferability of pretrained models to classifi- cation tasks,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Pactran: Pac-bayesian metrics for estimating the transferability of pretrained models to classifi- cation tasks,

Reference 2009

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Observation ae3c852b-f64e-4fd2-8eeb-907ef0fe5504 · outbound

This paper cites Federated opti- mization in heterogeneous networks,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Federated opti- mization in heterogeneous networks,

Reference 2010

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Observation d0122bf2-9bfd-4123-9f9e-c1005744c4a0 · outbound

This paper cites FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning.

Many-Task Federated Fine-Tuning via Unified Task Vectors FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning

Reference 2011

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This paper cites Federatedscope-llm: A com- prehensive package for fine-tuning large language models in fed- erated learning,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Federatedscope-llm: A com- prehensive package for fine-tuning large language models in fed- erated learning,

Reference 2013

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This paper cites Imagenet: A large-scale hierarchical im- age database.

Many-Task Federated Fine-Tuning via Unified Task Vectors Imagenet: A large-scale hierarchical im- age database

Reference 2014

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Observation 2aff5785-ca35-4be1-80f4-235e5237e8a9 · outbound

This paper cites Heterogeneous loRA for federated fine-tuning of on-device foundation models.

Many-Task Federated Fine-Tuning via Unified Task Vectors Heterogeneous loRA for federated fine-tuning of on-device foundation models

Reference 2017

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Observation c75963b9-52dc-4f35-81d3-89d25fef4ef3 · outbound

This paper cites Mas: Towards resource-efficient feder- ated multiple-task learning.

Many-Task Federated Fine-Tuning via Unified Task Vectors Mas: Towards resource-efficient feder- ated multiple-task learning

Reference 2018

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This paper cites Ezzeldin, Qingfeng Liu, Kee-Bong Song, Mostafa El-Khamy, and Salman Avestimehr.

Many-Task Federated Fine-Tuning via Unified Task Vectors Ezzeldin, Qingfeng Liu, Kee-Bong Song, Mostafa El-Khamy, and Salman Avestimehr

Reference 2019

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Observation a28190fb-cfc1-4d2f-9d24-093ba4821cab · outbound

This paper cites Many-task federated learning: A new problem set- ting and a simple baseline.

Many-Task Federated Fine-Tuning via Unified Task Vectors Many-task federated learning: A new problem set- ting and a simple baseline

Reference 2020

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Observation 1dfdc521-6ec8-4c24-beea-f13ba1446b0b · outbound

This paper cites Etran: Energy-based transferability estimation,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Etran: Energy-based transferability estimation,

Reference 2021

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Observation fce4018a-5dfb-4f68-9525-78bfe56bb0cb · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

Many-Task Federated Fine-Tuning via Unified Task Vectors Flower: A Friendly Federated Learning Research Framework

Reference 2022

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This paper cites An information-theoretic approach to transferability in task transfer learning,.

Many-Task Federated Fine-Tuning via Unified Task Vectors An information-theoretic approach to transferability in task transfer learning,

Reference 2023

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Observation a393c411-d306-4ab3-97c2-4b551ac1c218 · outbound

This paper cites Editing models with task arithmetic,.

Many-Task Federated Fine-Tuning via Unified Task Vectors Editing models with task arithmetic,

Reference 2024

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Observation 6f049c2b-c304-464a-818a-58dda84ef99d · outbound

This paper cites FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning.

Many-Task Federated Fine-Tuning via Unified Task Vectors FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning

Reference 2025

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Pith citing papers

Observation c6d452ac-b203-46ac-b953-497cfa60c4a1 · inbound

HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning cites this paper.

HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning Many-Task Federated Fine-Tuning via Unified Task Vectors

Reference 21

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