federated_learning.mbt

// 联邦学习与隐私计算（v43.0.0）

///|
fn __sqrt_approx(x : Double) -> Double {
  if x <= 0.0 {
    0.0
  } else {
    let mut x0 = x
    let mut y = (x0 + 1.0) / 2.0
    for _ in 0..<20 {
      x0 = y
      y = (x0 + x / x0) / 2.0
    }
    y
  }
}

// 联邦学习、差分隐私、同态加密、安全多方计算、隐私保护

///|
/// 联邦学习算法类型
pub(all) enum FederatedLearningAlgorithm {
  FedAvg
  FedProx
  FedAdam
  FedAdagrad
  FedYogi
  Scaffold
  FedNova
  FedDyn
  QFedAvg
  Unknown
} derive(Eq)

///|
/// 隐私保护技术类型
pub(all) enum PrivacyTechnology {
  DifferentialPrivacy
  HomomorphicEncryption
  SecureMultiPartyComputation
  FederatedLearning
  ZeroKnowledgeProof
  TrustedExecutionEnvironment
  SecureAggregation
  Anonymization
  Unknown
} derive(Eq)

///|
/// 差分隐私机制类型
pub(all) enum DPMechanism {
  Laplace
  Gaussian
  Exponential
  Staircase
  TruncatedLaplace
  Unknown
} derive(Eq)

///|
/// 同态加密类型
pub(all) enum HomomorphicEncryptionType {
  Paillier
  RSA
  ElGamal
  BFV
  BGV
  CKKS
  FHEW
  TFHE
  Unknown
} derive(Eq)

///|
/// 联邦学习客户端
pub(all) struct FederatedClient {
  client_id : Int
  data_size : Int
  local_epochs : Int
  learning_rate : Double
  batch_size : Int
  participation_rate : Double
  is_active : Bool
}

///|
/// 联邦学习配置
pub(all) struct FederatedLearningConfig {
  algorithm : FederatedLearningAlgorithm
  num_clients : Int
  num_rounds : Int
  client_fraction : Double
  local_epochs : Int
  learning_rate : Double
  batch_size : Int
  mu_proximal : Double
  use_differential_privacy : Bool
  dp_epsilon : Double
  dp_delta : Double
  dp_mechanism : DPMechanism
  use_secure_aggregation : Bool
  use_compression : Bool
  compression_ratio : Double
}

///|
/// 差分隐私配置
pub(all) struct DifferentialPrivacyConfig {
  epsilon : Double
  delta : Double
  mechanism : DPMechanism
  sensitivity : Double
  clipping_norm : Double
  use_noise_multiplier : Bool
  noise_multiplier : Double
  num_samples : Int
  sampling_rate : Double
}

///|
/// 同态加密配置
pub(all) struct HomomorphicEncryptionConfig {
  encryption_type : HomomorphicEncryptionType
  key_size : Int
  polynomial_degree : Int
  plaintext_modulus : Int
  coefficient_modulus : Array[Int]
  scale : Double
  use_batching : Bool
  use_relin : Bool
}

///|
/// 安全多方计算配置
pub(all) struct SecureMPCConfig {
  num_parties : Int
  protocol : String
  threshold : Int
  use_secret_sharing : Bool
  secret_sharing_type : String
  use_garbled_circuits : Bool
  use_oblivious_transfer : Bool
  communication_rounds : Int
}

///|
/// 联邦学习训练结果
pub(all) struct FederatedTrainingResult {
  num_rounds_completed : Int
  final_accuracy : Double
  final_loss : Double
  num_clients_participated : Int
  communication_cost_mb : Double
  training_time_s : Double
  convergence_round : Int
  privacy_budget_used : Double
}

///|
/// 隐私保护结果
pub(all) struct PrivacyProtectionResult {
  technology : PrivacyTechnology
  privacy_budget : Double
  epsilon : Double
  delta : Double
  accuracy_loss : Double
  overhead_time_ms : Double
  overhead_memory_mb : Double
  security_level : String
}

///|
/// 加密数据
pub(all) struct EncryptedData {
  ciphertext : Array[Int]
  encryption_type : HomomorphicEncryptionType
  key_id : Int
  size_bytes : Int
  is_encrypted : Bool
}

///|
/// 创建默认联邦学习配置
pub fn create_default_federated_config() -> FederatedLearningConfig {
  FederatedLearningConfig::{
    algorithm: FederatedLearningAlgorithm::FedAvg,
    num_clients: 10,
    num_rounds: 100,
    client_fraction: 0.5,
    local_epochs: 5,
    learning_rate: 0.01,
    batch_size: 32,
    mu_proximal: 0.01,
    use_differential_privacy: false,
    dp_epsilon: 1.0,
    dp_delta: 0.00001,
    dp_mechanism: DPMechanism::Gaussian,
    use_secure_aggregation: true,
    use_compression: false,
    compression_ratio: 0.5,
  }
}

///|
/// 创建默认差分隐私配置
pub fn create_default_dp_config() -> DifferentialPrivacyConfig {
  DifferentialPrivacyConfig::{
    epsilon: 1.0,
    delta: 0.00001,
    mechanism: DPMechanism::Gaussian,
    sensitivity: 1.0,
    clipping_norm: 1.0,
    use_noise_multiplier: true,
    noise_multiplier: 1.0,
    num_samples: 10000,
    sampling_rate: 0.01,
  }
}

///|
/// 创建默认同态加密配置
pub fn create_default_he_config() -> HomomorphicEncryptionConfig {
  HomomorphicEncryptionConfig::{
    encryption_type: HomomorphicEncryptionType::CKKS,
    key_size: 2048,
    polynomial_degree: 8192,
    plaintext_modulus: 65537,
    coefficient_modulus: [60, 40, 40, 60],
    scale: 10000000000000000000000000000000000000000.0,
    use_batching: true,
    use_relin: true,
  }
}

///|
/// 创建默认安全多方计算配置
pub fn create_default_mpc_config() -> SecureMPCConfig {
  SecureMPCConfig::{
    num_parties: 3,
    protocol: "BGW",
    threshold: 2,
    use_secret_sharing: true,
    secret_sharing_type: "Shamir",
    use_garbled_circuits: false,
    use_oblivious_transfer: true,
    communication_rounds: 10,
  }
}

///|
/// 创建联邦学习客户端
pub fn create_federated_clients(
  num_clients : Int,
  total_data_size : Int,
  config : FederatedLearningConfig,
) -> Array[FederatedClient] {
  let clients : Array[FederatedClient] = []
  let avg_data_size = total_data_size / num_clients

  for i in 0..<num_clients {
    let data_variation = (i % 3 - 1) * (avg_data_size / 5)
    let client_data_size = avg_data_size + data_variation

    clients.push(FederatedClient::{
      client_id: i,
      data_size: client_data_size,
      local_epochs: config.local_epochs,
      learning_rate: config.learning_rate,
      batch_size: config.batch_size,
      participation_rate: 0.8,
      is_active: true,
    })
  }

  clients
}

///|
/// 模拟联邦学习训练（简化版）
pub fn simulate_federated_training(
  clients : Array[FederatedClient],
  config : FederatedLearningConfig,
  initial_accuracy : Double,
) -> FederatedTrainingResult {
  let mut accuracy = initial_accuracy
  let mut loss = 1.0
  let mut convergence_round = config.num_rounds
  let mut total_clients_participated = 0
  let mut communication_cost = 0.0

  for round in 0..<config.num_rounds {
    // 选择参与客户端
    let num_participating = (clients.length().to_double() *
    config.client_fraction).to_int()
    total_clients_participated += num_participating

    // 模拟精度提升
    let improvement = 0.01 *
      (1.0 - accuracy) *
      (1.0 - round.to_double() / config.num_rounds.to_double())
    accuracy += improvement
    loss *= 0.95

    // 通信成本
    let model_size_mb = 100.0
    communication_cost += num_participating.to_double() * model_size_mb * 2.0

    // 检查收敛
    if accuracy > 0.95 && convergence_round == config.num_rounds {
      convergence_round = round + 1
    }
  }

  let privacy_budget_used = if config.use_differential_privacy {
    config.dp_epsilon * config.num_rounds.to_double() * config.client_fraction
  } else {
    0.0
  }

  FederatedTrainingResult::{
    num_rounds_completed: config.num_rounds,
    final_accuracy: accuracy,
    final_loss: loss,
    num_clients_participated: total_clients_participated,
    communication_cost_mb: communication_cost,
    training_time_s: config.num_rounds.to_double() * 10.0,
    convergence_round,
    privacy_budget_used,
  }
}

///|
/// 添加差分隐私噪声（简化版）
pub fn add_dp_noise(
  value : Double,
  config : DifferentialPrivacyConfig,
) -> Double {
  // 简化的噪声生成
  let noise_scale = match config.mechanism {
    DPMechanism::Laplace => config.sensitivity / config.epsilon
    DPMechanism::Gaussian => config.sensitivity * 2.0 / config.epsilon
    DPMechanism::Exponential => config.sensitivity / config.epsilon
    _ => config.sensitivity / config.epsilon
  }

  // 简化的伪随机噪声
  let noise = (value * 1000.0 % 1000.0 - 500.0) / 500.0 * noise_scale
  value + noise
}

///|
/// 同态加密（简化版）
pub fn encrypt_data(
  plaintext : Array[Double],
  config : HomomorphicEncryptionConfig,
) -> EncryptedData {
  let ciphertext : Array[Int] = []

  for value in plaintext {
    // 简化的加密：缩放+取模
    let scaled = (value * config.scale).to_int()
    let encrypted = scaled % config.plaintext_modulus
    ciphertext.push(encrypted)
  }

  EncryptedData::{
    ciphertext,
    encryption_type: config.encryption_type,
    key_id: 0,
    size_bytes: ciphertext.length() * 4,
    is_encrypted: true,
  }
}

///|
/// 同态解密（简化版）
pub fn decrypt_data(
  encrypted : EncryptedData,
  config : HomomorphicEncryptionConfig,
) -> Array[Double] {
  let plaintext : Array[Double] = []

  for value in encrypted.ciphertext {
    // 简化的解密
    let decrypted = value.to_double() / config.scale
    plaintext.push(decrypted)
  }

  plaintext
}

///|
/// 密文加法（简化版）
pub fn encrypted_add(
  a : EncryptedData,
  b : EncryptedData,
  config : HomomorphicEncryptionConfig,
) -> EncryptedData {
  let result : Array[Int] = []
  let length = if a.ciphertext.length() < b.ciphertext.length() {
    a.ciphertext.length()
  } else {
    b.ciphertext.length()
  }

  for i in 0..<length {
    let sum = (a.ciphertext[i] + b.ciphertext[i]) % config.plaintext_modulus
    result.push(sum)
  }

  EncryptedData::{
    ciphertext: result,
    encryption_type: config.encryption_type,
    key_id: a.key_id,
    size_bytes: result.length() * 4,
    is_encrypted: true,
  }
}

///|
/// 评估隐私保护效果（简化版）
pub fn evaluate_privacy_protection(
  original_accuracy : Double,
  protected_accuracy : Double,
  config : DifferentialPrivacyConfig,
) -> PrivacyProtectionResult {
  let accuracy_loss = original_accuracy - protected_accuracy
  let overhead_time = match config.mechanism {
    DPMechanism::Laplace => 1.0
    DPMechanism::Gaussian => 1.5
    DPMechanism::Exponential => 2.0
    _ => 1.0
  }

  let security_level = if config.epsilon < 0.1 {
    "very_high"
  } else if config.epsilon < 1.0 {
    "high"
  } else if config.epsilon < 10.0 {
    "medium"
  } else {
    "low"
  }

  PrivacyProtectionResult::{
    technology: PrivacyTechnology::DifferentialPrivacy,
    privacy_budget: config.epsilon,
    epsilon: config.epsilon,
    delta: config.delta,
    accuracy_loss,
    overhead_time_ms: overhead_time,
    overhead_memory_mb: 1.0,
    security_level,
  }
}

///|
/// 生成联邦学习与隐私计算报告
pub fn federated_learning_report_to_string(
  training_result : FederatedTrainingResult,
  privacy_result : PrivacyProtectionResult,
  config : FederatedLearningConfig,
) -> String {
  let mut report = "## 联邦学习与隐私计算报告\n\n"

  report = report + "### 联邦学习训练\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 算法 | " +
    (match config.algorithm {
      FederatedLearningAlgorithm::FedAvg => "FedAvg"
      FederatedLearningAlgorithm::FedProx => "FedProx"
      FederatedLearningAlgorithm::FedAdam => "FedAdam"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 完成轮次 | " +
    training_result.num_rounds_completed.to_string() +
    " |\n"
  report = report +
    "| 最终精度 | " +
    (training_result.final_accuracy * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 最终损失 | " +
    training_result.final_loss.to_string() +
    " |\n"
  report = report +
    "| 收敛轮次 | " +
    training_result.convergence_round.to_string() +
    " |\n"
  report = report +
    "| 参与客户端数 | " +
    training_result.num_clients_participated.to_string() +
    " |\n"
  report = report +
    "| 通信成本 | " +
    training_result.communication_cost_mb.to_string() +
    " MB |\n"
  report = report +
    "| 训练时间 | " +
    training_result.training_time_s.to_string() +
    " s |\n"
  report = report +
    "| 隐私预算使用 | " +
    training_result.privacy_budget_used.to_string() +
    " |\n"

  report = report + "\n### 隐私保护\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 技术 | " +
    (match privacy_result.technology {
      PrivacyTechnology::DifferentialPrivacy => "差分隐私"
      PrivacyTechnology::HomomorphicEncryption => "同态加密"
      PrivacyTechnology::SecureMultiPartyComputation => "安全多方计算"
      PrivacyTechnology::FederatedLearning => "联邦学习"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| Epsilon | " + privacy_result.epsilon.to_string() + " |\n"
  report = report + "| Delta | " + privacy_result.delta.to_string() + " |\n"
  report = report +
    "| 精度损失 | " +
    (privacy_result.accuracy_loss * 100.0).to_string() +
    "% |\n"
  report = report +
    "| 时间开销 | " +
    privacy_result.overhead_time_ms.to_string() +
    " ms |\n"
  report = report +
    "| 内存开销 | " +
    privacy_result.overhead_memory_mb.to_string() +
    " MB |\n"
  report = report + "| 安全级别 | " + privacy_result.security_level + " |\n"

  report
}
src\process\federated_learning\federated_learning.mbt

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