{
  "title": "Domain Adaptive Pretraining in Split Learning for Vision Foundation Models",
  "authors": [
    "Stan van Honk",
    "Vasileios Tsouvalas",
    "Nirvana Meratnia"
  ],
  "version": "Author manuscript compiled 13 October 2026",
  "manuscript_sha256": "34993089d8f5ea0f7dd07c12ff4f9eb2e8fbf6a64634c951289b29d447c3862c",
  "methodology": {
    "source": "PDF sections 2 and 3, pages 2-3; Fig. 1",
    "backbone": "DINOv2-pretrained ViT-B/14",
    "clients": 10,
    "partition": "identical IID partitions for DAPT and fine-tuning",
    "domain_scope": "three independent same-domain vision experiments",
    "split": "client keeps first transformer block frozen; server hosts remaining blocks",
    "server_training": "last transformer block fully unfrozen (last two for CheXpert); other server blocks adapted with LoRA",
    "phase1": "Clients form 2 global and 8 local crops from unlabeled in-domain images, compute frozen frontend features, and upload CLS and patch tokens. Server concatenates all client features, trains the DINOv2 student using self-supervision and updates an EMA teacher. iBOT masking is applied on the server. No gradients return to clients.",
    "phase2": "The adapted teacher backbone is frozen. Clients send features of original non-augmented labeled images, server returns CLS representation for classification, and each client trains its task-specific head locally. No task-head gradients are sent to the server.",
    "crop_pixels": {
      "fMoW": {
        "global": 224,
        "local": 98
      },
      "QuickDraw": {
        "global": 224,
        "local": 98
      },
      "CheXpert": {
        "global": 518,
        "local": 196
      }
    },
    "task_data": "DAPT uses training images without their labels; downstream fine-tuning uses only labeled subset. CheXpert training is evaluated on the CheXlocalize test set.",
    "fewshot_sampling": "Each client samples k-shot data with a client-specific seed shared across methods; numerical seeds are not given.",
    "dino_naming": "DINOv2 is the pretrained model and continued pretraining recipe. L_DINO is one term of the DINOv2 objective, alongside iBOT and KoLeo; calling the method DINO-only would omit those terms."
  },
  "equations": [
    {
      "id": "feature_split",
      "source": "section 2.1, page 2",
      "latex": "(h_{\\mathrm{cls}},\\mathbf H)=f_{\\theta_c}(x),\\qquad (z_{\\mathrm{cls}},\\mathbf Z)=f_{\\theta_s}(h_{\\mathrm{cls}},\\mathbf H)"
    },
    {
      "id": "global_batch",
      "source": "section 2.1, page 2",
      "latex": "\\mathcal B^U=\\bigcup_{n\\in\\mathcal N}\\mathcal B_n^U"
    },
    {
      "id": "ssl_loss",
      "source": "Eq. (1), page 2",
      "latex": "\\mathcal L_{\\mathrm{SSL}}=\\mathcal L_{\\mathrm{DINO}}+\\lambda_1\\mathcal L_{\\mathrm{iBOT}}+\\lambda_2\\mathcal L_{\\mathrm{KoLeo}}"
    },
    {
      "id": "task_loss",
      "source": "Eq. (2), page 3",
      "latex": "\\mathcal L_{\\mathrm{FT}}^{(n)}=\\mathcal L_{\\mathrm{sup}}\\!\\left(g_{\\phi_n}(r_n(x)),y\\right)"
    }
  ],
  "table1": {
    "source": "PDF Table 1, page 3",
    "conditions": [
      "4-shot",
      "8-shot",
      "16-shot",
      "LP"
    ],
    "methods": [
      "FT w/o DAPT",
      "FedAvg",
      "MocoSFL",
      "SDAPT"
    ],
    "datasets": [
      {
        "dataset": "fMoW",
        "metric": "Accuracy",
        "score_unit": "percent",
        "direction": "higher is better",
        "rows": [
          {
            "method": "FT w/o DAPT",
            "scores": [
              12.8,
              18.3,
              23.4,
              53.9
            ],
            "gains_over_ft_pp": [
              0.0,
              0.0,
              0.0,
              0.0
            ]
          },
          {
            "method": "FedAvg",
            "scores": [
              18.4,
              28.8,
              38.8,
              67.3
            ],
            "gains_over_ft_pp": [
              5.6,
              10.5,
              15.4,
              13.4
            ]
          },
          {
            "method": "MocoSFL",
            "scores": [
              22.4,
              31.5,
              35.7,
              62.1
            ],
            "gains_over_ft_pp": [
              9.6,
              13.2,
              12.3,
              8.2
            ]
          },
          {
            "method": "SDAPT",
            "scores": [
              16.9,
              27.5,
              37.5,
              66.7
            ],
            "gains_over_ft_pp": [
              4.1,
              9.2,
              14.1,
              12.8
            ]
          }
        ],
        "mean_sdapt_fewshot_gain_pp": 9.13333333
      },
      {
        "dataset": "CheXpert",
        "metric": "AUROC",
        "score_unit": "AUROC on 0-100 scale",
        "direction": "higher is better",
        "rows": [
          {
            "method": "FT w/o DAPT",
            "scores": [
              34.7,
              38.1,
              37.8,
              77.4
            ],
            "gains_over_ft_pp": [
              0.0,
              0.0,
              0.0,
              0.0
            ]
          },
          {
            "method": "FedAvg",
            "scores": [
              37.7,
              39.3,
              43.9,
              78.1
            ],
            "gains_over_ft_pp": [
              3.0,
              1.2,
              6.1,
              0.7
            ]
          },
          {
            "method": "MocoSFL",
            "scores": [
              39.3,
              39.7,
              43.4,
              80.2
            ],
            "gains_over_ft_pp": [
              4.6,
              1.6,
              5.6,
              2.8
            ]
          },
          {
            "method": "SDAPT",
            "scores": [
              41.3,
              41.7,
              44.4,
              80.7
            ],
            "gains_over_ft_pp": [
              6.6,
              3.6,
              6.6,
              3.3
            ]
          }
        ],
        "mean_sdapt_fewshot_gain_pp": 5.6
      },
      {
        "dataset": "QuickDraw",
        "metric": "Accuracy",
        "score_unit": "percent",
        "direction": "higher is better",
        "rows": [
          {
            "method": "FT w/o DAPT",
            "scores": [
              18.4,
              25.8,
              32.3,
              42.9
            ],
            "gains_over_ft_pp": [
              0.0,
              0.0,
              0.0,
              0.0
            ]
          },
          {
            "method": "FedAvg",
            "scores": [
              27.0,
              37.2,
              43.9,
              50.8
            ],
            "gains_over_ft_pp": [
              8.6,
              11.4,
              11.6,
              7.9
            ]
          },
          {
            "method": "MocoSFL",
            "scores": [
              23.2,
              32.4,
              39.0,
              48.1
            ],
            "gains_over_ft_pp": [
              4.8,
              6.6,
              6.7,
              5.2
            ]
          },
          {
            "method": "SDAPT",
            "scores": [
              22.3,
              33.2,
              43.1,
              51.1
            ],
            "gains_over_ft_pp": [
              3.9,
              7.4,
              10.8,
              8.2
            ]
          }
        ],
        "mean_sdapt_fewshot_gain_pp": 7.36666667
      }
    ],
    "mean_sdapt_fewshot_gain_pp_calculated": 7.36666667,
    "mean_sdapt_fewshot_gain_pp_reported": 7.4,
    "mean_scope": "Unweighted average over 9 few-shot settings; excludes LP."
  },
  "table2": {
    "source": "PDF Table 2, page 3; interpretation page 4",
    "adaptation_dataset": "CheXpert",
    "evaluation_dataset": "CheXlocalize test set",
    "metric": "AUROC on 0-100 scale",
    "direction": "higher is better",
    "rows": [
      {
        "category": "Cardiomediastinal",
        "label_as_printed": "Enlarged cardiom.",
        "FT w/o DAPT": 51.72,
        "MocoSFL": 57.02,
        "SDAPT": 57.18,
        "mocosfl_gain_pp": 5.3,
        "sdapt_gain_pp": 5.46
      },
      {
        "category": "Cardiomediastinal",
        "label_as_printed": "Cardiomegaly",
        "FT w/o DAPT": 72.0,
        "MocoSFL": 76.51,
        "SDAPT": 78.42,
        "mocosfl_gain_pp": 4.51,
        "sdapt_gain_pp": 6.42
      },
      {
        "category": "Pulmonary",
        "label_as_printed": "Lung opacity",
        "FT w/o DAPT": 86.03,
        "MocoSFL": 88.43,
        "SDAPT": 87.22,
        "mocosfl_gain_pp": 2.4,
        "sdapt_gain_pp": 1.19
      },
      {
        "category": "Pulmonary",
        "label_as_printed": "Edema",
        "FT w/o DAPT": 90.04,
        "MocoSFL": 91.22,
        "SDAPT": 87.79,
        "mocosfl_gain_pp": 1.18,
        "sdapt_gain_pp": -2.25
      },
      {
        "category": "Pulmonary",
        "label_as_printed": "Consolidation",
        "FT w/o DAPT": 75.37,
        "MocoSFL": 75.59,
        "SDAPT": 75.09,
        "mocosfl_gain_pp": 0.22,
        "sdapt_gain_pp": -0.28
      },
      {
        "category": "Pulmonary",
        "label_as_printed": "Atelectasis",
        "FT w/o DAPT": 75.95,
        "MocoSFL": 78.81,
        "SDAPT": 76.22,
        "mocosfl_gain_pp": 2.86,
        "sdapt_gain_pp": 0.27
      },
      {
        "category": "Pleural",
        "label_as_printed": "Pleural effusion",
        "FT w/o DAPT": 88.61,
        "MocoSFL": 89.18,
        "SDAPT": 89.97,
        "mocosfl_gain_pp": 0.57,
        "sdapt_gain_pp": 1.36
      },
      {
        "category": "Devices",
        "label_as_printed": "Support devices",
        "FT w/o DAPT": 79.57,
        "MocoSFL": 85.01,
        "SDAPT": 93.58,
        "mocosfl_gain_pp": 5.44,
        "sdapt_gain_pp": 14.01
      }
    ],
    "reported_average": {
      "FT w/o DAPT": 77.41,
      "MocoSFL": 80.22,
      "SDAPT": 80.68,
      "mocosfl_gain_pp": 2.81,
      "sdapt_gain_pp": 3.27
    },
    "sdapt_improved_labels": 6,
    "total_labels": 8
  },
  "figure2": {
    "source": "PDF Fig. 2, page 4",
    "x_axes": {
      "samples_seen": {
        "unit": "cumulative sample presentations",
        "direction": "less cost for equal performance"
      },
      "communication_tb": {
        "unit": "decimal TB",
        "scope": "modeled BF16 feature uplink during DAPT",
        "direction": "less cost for equal performance"
      },
      "client_compute_gflops": {
        "unit": "GFLOPs",
        "scope": "all-client forward computation during DAPT, excluding server",
        "direction": "less cost for equal performance"
      }
    },
    "y_axis": {
      "unit": "percentage points",
      "definition": "checkpoint score minus dataset-specific displayed Table 1 LP no-DAPT baseline",
      "direction": "higher is better",
      "metric_by_dataset": {
        "fMoW": "accuracy gain",
        "QuickDraw": "accuracy gain",
        "CheXpert": "AUROC gain"
      }
    },
    "series": [
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    "no_interpolation_or_extrapolation": true,
    "budget_costs_are_estimates": true
  },
  "notes": [
    "Table 1 scores and Table 2 changes are preserved exactly. Reported 7.4 pp averages nine few-shot gains, mixing accuracy and AUROC differences on 0–100 scales; it is not relative percent improvement.",
    "All 56 Figure 2 checkpoints are plotted on each cost axis. Sample counts denote cumulative presentations including repeats. Costs exclude server work and are estimates, not measured latency or energy.",
    "Table 1 LP results and Figure 2 checkpoint results remain separate because rounded reference scores and checkpoint selection differ. No uncertainty intervals are supplied.",
    "Classification with 10 IID clients; dense prediction and heterogeneous partitions are not evaluated here.",
    "Figure 2 uses the original plotting data; its source vector stream is identical to the figure embedded in the manuscript."
  ]
}