---
id: wiki-2026-0508-ieee-p36521
title: IEEE P3652.1 (Federated ML Standard)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [IEEE 3652.1-2020, IEEE Federated ML Standard, P3652.1]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [federated-learning, ieee, standards, privacy, ml]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: flower-pysyft
---

# IEEE P3652.1 (Federated ML Standard)

## 매 한 줄
> **"매 federated learning 의 첫 공식 표준 — 누가 누구와 무엇을 어떻게 공유하는지를 정의한다"**. IEEE 3652.1-2020 (Guide for Architectural Framework and Application of Federated Machine Learning) 은 WeBank/Tencent/Microsoft 등이 주도해 horizontal/vertical/federated transfer learning 의 분류, 참여자 역할, 보안 요구사항을 표준화한 첫 공식 문서로, 현재 GDPR/HIPAA/금융권 cross-silo 학습의 reference 가 되었다.

## 매 핵심

### 매 3 가지 federated learning 분류 (3652.1)
- **Horizontal FL (HFL)**: 같은 feature space, 다른 sample (예: 여러 병원의 동일 항목 환자 데이터).
- **Vertical FL (VFL)**: 같은 sample 일부, 다른 feature (예: 은행 + 이커머스가 공통 고객의 다른 속성).
- **Federated Transfer Learning (FTL)**: feature/sample 둘 다 부분 겹침 — transfer learning 결합.

### 매 참여자 역할
- **Data Owner / Client**: 로컬 데이터 보유, 로컬 학습 수행.
- **Coordinator / Aggregator**: 모델 파라미터/그래디언트 집계 (FedAvg 등).
- **Auditor**: privacy/compliance 검증.
- **Model Consumer**: 최종 모델 사용자.

### 매 보안/프라이버시 요구사항
- secure aggregation (cryptographic) 권장.
- differential privacy 옵션.
- 통신 채널 암호화 (TLS 1.3+).
- model inversion / membership inference 위험 평가.
- audit log + reproducibility.

### 매 응용
1. Cross-hospital 의료 영상 모델 (HFL).
2. 은행 + 통신사 신용평가 (VFL).
3. 모바일 키보드 next-word prediction (Gboard, HFL on-device).
4. 광고 conversion modeling (clean room + FL).

## 💻 패턴

### 1. Flower (FL framework) — HFL 클라이언트
```python
import flwr as fl
import torch

class HospitalClient(fl.client.NumPyClient):
    def __init__(self, model, train_loader, val_loader):
        self.model, self.train, self.val = model, train_loader, val_loader

    def get_parameters(self, config):
        return [v.cpu().numpy() for v in self.model.state_dict().values()]

    def set_parameters(self, params):
        sd = {k: torch.tensor(v) for k, v in zip(self.model.state_dict(), params)}
        self.model.load_state_dict(sd, strict=True)

    def fit(self, params, config):
        self.set_parameters(params)
        train_one_epoch(self.model, self.train)
        return self.get_parameters({}), len(self.train.dataset), {}

    def evaluate(self, params, config):
        self.set_parameters(params)
        loss, acc = eval_model(self.model, self.val)
        return float(loss), len(self.val.dataset), {"acc": acc}

fl.client.start_numpy_client(server_address="agg.example:8443", client=HospitalClient(...))
```

### 2. FedAvg aggregator (Flower server)
```python
import flwr as fl
strategy = fl.server.strategy.FedAvg(
    min_fit_clients=5, min_available_clients=5,
    fraction_fit=1.0, fraction_evaluate=1.0,
)
fl.server.start_server(
    server_address="0.0.0.0:8443",
    config=fl.server.ServerConfig(num_rounds=20),
    strategy=strategy,
)
```

### 3. Secure aggregation (PySyft / Flower SecAgg+)
```python
from flwr.common import SecAggPlusWorkflow
workflow = SecAggPlusWorkflow(
    num_shares=3, reconstruction_threshold=2, max_weight=16384,
)
# server: clients 가 mask 적용 후 전송 — server 는 합계만 복원, 개별 불가
```

### 4. Differential Privacy (Opacus)
```python
from opacus import PrivacyEngine
engine = PrivacyEngine()
model, optimizer, train_loader = engine.make_private_with_epsilon(
    module=model, optimizer=optimizer, data_loader=train_loader,
    target_epsilon=3.0, target_delta=1e-5, epochs=10, max_grad_norm=1.0,
)
```

### 5. VFL — split learning skeleton (PyTorch)
```python
# Bank: bottom model on transactions
class BottomBank(nn.Module):
    def forward(self, x): return self.net(x)  # -> embed_bank

# Telco: bottom model on call patterns
class BottomTelco(nn.Module): ...  # -> embed_telco

# Aggregator (top): concat + classify
class Top(nn.Module):
    def forward(self, eb, et): return self.head(torch.cat([eb, et], dim=-1))
# 학습: client 는 embed 만 송신, gradient 만 수신
```

### 6. Audit log (3652.1 Annex B 권장)
```json
{
  "round": 7,
  "ts": "2026-05-10T09:00:00Z",
  "participants": ["hosp-a", "hosp-b", "hosp-c"],
  "aggregation": "FedAvg",
  "secagg": "SecAgg+",
  "dp": { "epsilon": 3.0, "delta": 1e-5 },
  "model_hash": "sha256:...",
  "signed_by": "ed25519:..."
}
```

### 7. Cross-silo deployment (Kubernetes manifest 일부)
```yaml
apiVersion: apps/v1
kind: Deployment
metadata: { name: fl-client-hospA, namespace: hospital-a }
spec:
  template:
    spec:
      containers:
        - name: client
          image: registry/fl-client:1.4
          env:
            - { name: AGGREGATOR_URL, value: "https://agg.consortium.example:8443" }
            - { name: TLS_CA, valueFrom: { secretKeyRef: { name: ca, key: ca.crt } } }
            - { name: SITE_ID, value: "hosp-a" }
          volumeMounts:
            - { name: data, mountPath: /data, readOnly: true }
```

### 8. Membership inference attack 검증
```python
# attacker tries to infer if a sample was in training set
def attack_score(model, x):
    with torch.no_grad():
        return model(x).softmax(-1).max().item()
# 학습 / 비학습 sample 의 score 분포 차이 → AUC 0.5 에 가까울수록 안전
```

### 9. participant onboarding checklist
```yaml
participant: hospital-c
checklist:
  - data_governance_signed: true
  - dpa_signed: true
  - tls_cert_valid_until: 2027-01-01
  - schema_version: v3
  - feature_alignment_test: passed
  - privacy_budget_allocated: { epsilon: 5.0, delta: 1e-5 }
```

### 10. FATE (WeBank reference impl) job (KubeFATE)
```yaml
# horizontal_lr.yaml
component_parameters:
  common:
    homo_lr_0:
      penalty: L2
      max_iter: 30
      learning_rate: 0.1
role:
  guest: { "0": { reader_0: { table: { name: hosp_a, namespace: hetero } } } }
  host:  { "0": { reader_0: { table: { name: hosp_b, namespace: hetero } } } }
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 같은 schema, 여러 사이트 | HFL (FedAvg) |
| 같은 user, 다른 feature | VFL (split learning) |
| 일부 겹침 | FTL |
| 모바일 device 수만 명 | Cross-device FL (Flower / TFF) |
| 규제 산업 cross-org | 3652.1 + SecAgg + DP + audit log |

**기본값**: Cross-silo (수십 사이트) 는 Flower + SecAgg+ + DP(eps≤8) + 3652.1 audit log.

## 🔗 Graph
- 부모: [[Federated-Learning]] · [[IEEE-Standards]]
- 변형: [[Cross-Device-FL]] · [[Cross-Silo-FL]]
- 응용: [[Healthcare-FL]] · [[Finance-FL]]
- Adjacent: [[Differential-Privacy]] · [[Secure-Aggregation]] · [[GDPR]]

## 🤖 LLM 활용
**언제**: 3652.1 의 분류 (HFL/VFL/FTL) 매핑, audit log schema 초안, threat model checklist.
**언제 X**: 실제 cryptographic protocol 구현 — 검증된 lib (Flower, FATE) 사용, LLM 자작 금지.

## ❌ 안티패턴
- **secagg 없는 raw gradient 송신**: gradient inversion 으로 raw data 복원 가능.
- **DP 없이 over-fitting model 공개**: membership inference 위험.
- **audit log 미보존**: 규제 incident 시 책임 분리 불가.
- **schema drift 무시**: client 마다 다른 feature order → silent corruption.
- **drop-out client 처리 누락**: SecAgg 가 reconstruction_threshold 미달 시 round 실패.

## 🧪 검증 / 중복
- Verified (IEEE 3652.1-2020 official PDF, Flower docs 1.x, FATE docs 2026).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 3652.1 분류 + Flower/FATE 패턴 + SecAgg/DP |
