Coverage for integrations / agent_engine / federated_gradient_protocol.py: 97.2%

1""" 

2Federated Gradient Protocol — Phase 2 stubs for LoRA gradient sync. 

3 

4Phase 2 will implement: 

5- LoRA gradient types (sparse, rank-4, ~4KB/layer) 

6- Byzantine-resilient aggregation (Krum, coordinate-wise median) 

7- Differential privacy noise injection 

8- Gradient compression with error feedback 

9 

10These are interface definitions and placeholder implementations. 

11Phase 1 (embedding sync) is fully functional in embedding_delta.py + gradient_service.py. 

12""" 

13import logging 

14from typing import Dict, List, Optional 

15 

16logger = logging.getLogger('hevolve_social') 

17 

18 

19# ─── LoRA Gradient Types ─── 

20 

21class LoRAGradient: 

22 """Placeholder: Low-Rank Adaptation gradient for a single layer. 

23 

24 In Phase 2, this will hold: 

25 - layer_name: str (e.g., 'attention.q_proj') 

26 - rank: int (typically 4) 

27 - delta_A: compressed matrix (input projection delta) 

28 - delta_B: compressed matrix (output projection delta) 

29 - metadata: node_id, timestamp, signature 

30 """ 

31 

32 def __init__(self, layer_name: str = '', rank: int = 4): 

33 self.layer_name = layer_name 

34 self.rank = rank 

35 self.delta_A: Optional[list] = None # Will be numpy array in Phase 2 

36 self.delta_B: Optional[list] = None 

37 self.node_id: str = '' 

38 self.signature: str = '' 

39 

40 def to_dict(self) -> Dict: 

41 return { 

42 'layer_name': self.layer_name, 

43 'rank': self.rank, 

44 'node_id': self.node_id, 

45 'phase': 2, 

46 'status': 'stub', 

47 } 

48 

49 def estimated_size_bytes(self) -> int: 

50 """Estimated transmission size. LoRA rank-4 ≈ 4KB per layer.""" 

51 return self.rank * 2 * 512 * 4 # rank × 2 matrices × hidden × float32 

52 

53 

54# ─── Byzantine Aggregation Interface ─── 

55 

56class ByzantineAggregator: 

57 """Placeholder: Byzantine-resilient gradient aggregation. 

58 

59 Phase 2 will implement: 

60 - Krum: Select the gradient closest to all others 

61 - Coordinate-wise median: Per-element median across peers 

62 - Trimmed mean: Already implemented in Phase 1 for embeddings 

63 """ 

64 

65 METHODS = ['krum', 'coordinate_median', 'trimmed_mean'] 

66 

67 def __init__(self, method: str = 'trimmed_mean', 

68 byzantine_fraction: float = 0.2): 

69 self.method = method 

70 self.byzantine_fraction = byzantine_fraction 

71 

72 def aggregate(self, gradients: List[LoRAGradient]) -> Optional[LoRAGradient]: 

73 """Aggregate gradients from multiple peers. 

74 

75 Not implemented in Phase 2 stub. Returns None. 

76 """ 

77 logger.debug(f"ByzantineAggregator.aggregate() called — Phase 2 stub " 

78 f"(method={self.method}, gradients={len(gradients)})") 

79 return None 

80 

81 def detect_byzantine(self, gradients: List[LoRAGradient]) -> List[str]: 

82 """Detect potentially Byzantine gradient submissions. 

83 

84 Returns list of suspicious node_ids. Not implemented in Phase 2 stub. 

85 """ 

86 return [] 

87 

88 def get_status(self) -> Dict: 

89 return { 

90 'method': self.method, 

91 'byzantine_fraction': self.byzantine_fraction, 

92 'phase': 2, 

93 'status': 'stub', 

94 'available_methods': self.METHODS, 

95 } 

96 

97 

98# ─── Differential Privacy (Stub) ─── 

99 

100class DifferentialPrivacyNoise: 

101 """Placeholder: Gaussian noise injection for gradient privacy. 

102 

103 Phase 2 will add calibrated Gaussian noise to gradients before 

104 transmission to ensure (epsilon, delta)-differential privacy. 

105 """ 

106 

107 def __init__(self, epsilon: float = 1.0, delta: float = 1e-5, 

108 clip_norm: float = 1.0): 

109 self.epsilon = epsilon 

110 self.delta = delta 

111 self.clip_norm = clip_norm 

112 

113 def add_noise(self, gradient: LoRAGradient) -> LoRAGradient: 

114 """Add calibrated noise to gradient. Phase 2 stub — returns unchanged.""" 

115 return gradient 

116 

117 def get_privacy_budget(self) -> Dict: 

118 return { 

119 'epsilon': self.epsilon, 

120 'delta': self.delta, 

121 'clip_norm': self.clip_norm, 

122 'phase': 2, 

123 'status': 'stub', 

124 }