F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics | Paper Citation Record

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This paper cites Ben Abacha, Vivek V.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Ben Abacha, Vivek V

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This paper cites Federated Learning Based on Dynamic Regularization.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Federated Learning Based on Dynamic Regularization

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This paper cites Federated Learning with Personalization Layers.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Federated Learning with Personalization Layers

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This paper cites Artificial bee colony algorithm: a survey.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Artificial bee colony algorithm: a survey

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This paper cites Strong Baselines for Parameter Efficient Few-Shot Fine-tuning.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Strong Baselines for Parameter Efficient Few-Shot Fine-tuning

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This paper cites Vqa-med: Overview of the medical visual question answering task at imageclef 2019.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Vqa-med: Overview of the medical visual question answering task at imageclef 2019

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This paper cites Overview of the vqa-med task at imageclef 2021: Visual question answer- ing and generation in the medical domain.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Overview of the vqa-med task at imageclef 2021: Visual question answer- ing and generation in the medical domain

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This paper cites Bit- Fit: Simple parameter-efficient fine-tuning for transformer- based masked language-models.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Bit- Fit: Simple parameter-efficient fine-tuning for transformer- based masked language-models

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Unresolved cited work

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This paper cites A brief introduction to the neural tan- gent kernel.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics A brief introduction to the neural tan- gent kernel

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This paper cites Tinytl: Reduce memory, not parameters for efficient on-device learning.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Tinytl: Reduce memory, not parameters for efficient on-device learning

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This paper cites Towards Understanding the Spectral Bias of Deep Learning.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Towards Understanding the Spectral Bias of Deep Learning

Reference 12

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This paper cites Efficient personalized federated learning via sparse model-adaptation.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Efficient personalized federated learning via sparse model-adaptation

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This paper cites Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning

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This paper cites On Bridging Generic and Personalized Federated Learning for Image Classification.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics On Bridging Generic and Personalized Federated Learning for Image Classification

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This paper cites FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

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This paper cites Metafed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Metafed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare

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This paper cites Mopso: A proposal for multiple objective particle swarm optimiza- tion.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Mopso: A proposal for multiple objective particle swarm optimiza- tion

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This paper cites Exploiting shared representations for personal- ized federated learning.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Exploiting shared representations for personal- ized federated learning

Reference 19

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This paper cites A fast and elitist multiobjective genetic algo- rithm: Nsga-ii.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics A fast and elitist multiobjective genetic algo- rithm: Nsga-ii

Reference 20

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Ant colony optimization

Reference 21

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Resist: Layer-wise decomposition of resnets for distributed training

Reference 22

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This paper cites Per- sonalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Per- sonalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

Reference 23

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Schwab, and Ari S

Reference 24

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This paper cites Promptfl: Let federated participants cooper- atively learn prompts instead of models-federated learning in age of foundation model.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Promptfl: Let federated participants cooper- atively learn prompts instead of models-federated learning in age of foundation model

Reference 25

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Learn- ing both weights and connections for efficient neural net- work

Reference 26

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Sensitivity-aware visual parameter-efficient fine- tuning

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This paper cites PathVQA: 30000+ Questions for Medical Visual Question Answering.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics PathVQA: 30000+ Questions for Medical Visual Question Answering

Reference 28

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Re- view of federated learning and machine learning-based methods for medical image analysis

Reference 29

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Scaling feder- ated learning for fine-tuning of large language models

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Parameter-efficient transfer learning for nlp

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics LoRA: Low-rank adaptation of large language models

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Omnimedvqa: A new large-scale comprehensive evaluation benchmark for medical lvlm

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Neu- ral tangent kernel: Convergence and generalization in neural networks

Reference 34

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Vi- sual prompt tuning

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Less is More: Selective Layer Finetuning with SubTuning

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Scaffold: Stochastic controlled averaging for feder- ated learning

Reference 37

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Vilt: Vision- and-language transformer without convolution or region su- pervision

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Revealing the Dark Secrets of BERT

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics A dataset of clinically generated visual questions and answers about radiology images

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Mixout: Effective regularization to finetune large-scale pre- trained language models

Reference 41

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

Reference 42

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 43

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Layer- wise adaptive model aggregation for scalable federated learning

Reference 44

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 45

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 46

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 48

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 49

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Federated learning: Challenges, methods, and future directions

Reference 50

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 51

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

Reference 52

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Slake: A semantically-labeled knowledge- enhanced dataset for medical visual question answering

Reference 53

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Improved baselines with visual instruction tuning

Reference 54

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Federated Representation Learning in the Under-Parameterized Regime

Reference 55

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Personalized feder- ated learning with adaptive batchnorm for healthcare

Reference 56

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics A gradient flow framework for analyzing network pruning

Reference 57

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Communication- efficient learning of deep networks from decentralized data

Reference 58

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning

Reference 59

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Federated learning with partial model personalization

Reference 60

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Winning the lottery ahead of time: Efficient early network pruning

Reference 61

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics On the spectral bias of neural networks

Reference 62

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Efficient parametrization of multi-domain deep neural net- works

Reference 63

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics AdapterDrop: On the Efficiency of Adapters in Transformers

Reference 64

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Re- thinking semi-supervised federated learning: How to co- train fully-labeled and fully-unlabeled client imaging data

Reference 65

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Observation 24b34591-6c62-4b36-9d29-a72d06b6aba1 · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Alison Noble

Reference 66

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Observation ab236f6d-066f-445a-b29e-0d3428895b07 · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning

Reference 67

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Partial is better than all: Revisiting fine- tuning strategy for few-shot learning

Reference 68

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Train faster, perform better: mod- ular adaptive training in over-parameterized models

Reference 69

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Exploring parameter-efficient fine-tuning for improv- ing communication efficiency in federated learning

Reference 70

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Training neu- ral networks with fixed sparse masks

Reference 71

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Fedselect: Personalized fed- erated learning with customized selection of parameters for fine-tuning

Reference 72

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Fedproto: Federated proto- type learning across heterogeneous clients

Reference 73

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Pruning neural networks without any data by iter- atively conserving synaptic flow

Reference 74

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Three things everyone should know about vision transformers

Reference 75

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Simulated annealing

Reference 76

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Post-deployment adaptation with access to source data via federated learning and source-target remote gradient alignment

Reference 77

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities

Reference 78

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 79

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Tackling the objective inconsistency prob- lem in heterogeneous federated optimization

Reference 80

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Federated dropout—a simple approach for enabling federated learning on resource constrained devices

Reference 81

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Observation 6961220b-cbd0-4ea3-a6e4-6d8f6ef38593 · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Personalized Federated Learning with Feature Alignment and Classifier Collaboration

Reference 82

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Reference 83

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Exploring one-shot semi-supervised federated learning with pre-trained diffusion models

Reference 84

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Fedas: Bridg- ing inconsistency in personalized federated learning

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:53:07.183702Z

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Observation e3ee4d4f-c413-46e7-8b5a-0d3671904afc · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Reference 86

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models

Reference 87

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Fedala: Adaptive local aggregation for personalized federated learning

Reference 88

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Observation bec1d67d-9e50-42b3-95e9-d28167e1ddbb · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics To- wards building the federatedgpt: Federated instruction tun- ing

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:53:07.158158Z

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Observation 1683f929-f7d0-4bcc-970c-c50716e43164 · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model

Reference 90

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Fine-tuning global model via data-free knowledge distillation for non-iid federated learning

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:53:07.144840Z

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Observation c307dc5a-427e-492d-92a8-0bf9688ccba6 · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Personalized Federated Learning with First Order Model Optimization

Reference 92

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Pruning Foundation Models for High Accuracy without Retraining

Reference 93

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Observation b77114eb-d9e4-4d76-8ad8-fb80df2da702 · outbound

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F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

Reference 94

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