fedops - npm Package Compare versions

Comparing version 1.1.30.4 to 1.1.30.5

fedops.egg-info/PKG-INFO

@@ -1,4 +0,4 @@
-Metadata-Version: 2.4
+Metadata-Version: 2.2
 Name: fedops
-Version: 1.1.30.4
+Version: 1.1.30.5
 Summary: FL Lifecycle Operations Management Platform
@@ -5,0 +5,0 @@ Home-page: https://github.com/gachon-CCLab/FedOps.git

fedops/client/client_fl.py

@@ -124,5 +124,9 @@ #client/client_fl.py
         # Get hyperparameters for this round
-        batch_size: int = config.get("batch_size", self.cfg.batch_size)
-        epochs: int = config.get("local_epochs", self.cfg.num_epochs)
-        num_rounds: int = config.get("num_rounds", self.cfg.num_rounds)
+        # batch_size: int = config.get("batch_size", self.cfg.batch_size)
+        # epochs: int = config.get("local_epochs", self.cfg.num_epochs)
+        # num_rounds: int = config.get("num_rounds", self.cfg.num_rounds)
+        # Get hyperparameters for this round
+        batch_size: int = config["batch_size"]
+        epochs: int = config["local_epochs"]
+        num_rounds: int = config["num_rounds"]
 
@@ -133,2 +137,4 @@ # HPO override (서버 전략에서 내려준 값) 하이퍼파라미터 튜닝을 하게되면 바뀌니까.
         hp_ep = config.get("hp_local_epochs", None) # ✨
+
+        
         if hp_bs is not None: # ✨
@@ -289,3 +295,4 @@ batch_size = int(hp_bs) # ✨
         # Get config values
-        batch_size: int = config.get("batch_size", self.cfg.batch_size)
+        # batch_size: int = config.get("batch_size", self.cfg.batch_size)
+        batch_size: int = config["batch_size"]
 
@@ -313,3 +320,3 @@ # Initialize test_loss, test_accuracy
             test_loss, test_accuracy, metrics = self.test_torch(self.model, self.test_loader, self.cfg)
-            num_examples_test = len(self.test_loader.dataset) if hasattr(self.test_loader, "dataset") else len(self.test_loader)
+            num_examples_test = len(self.test_loader)
         
@@ -316,0 +323,0 @@ elif self.model_type == "Huggingface":

fedops/server/fedmap/strategy.py

@@ -102,2 +102,16 @@ # fedmap/strategy.py
 
+# --------- small helpers to read env knobs (optional) --------- #
+def _get_int_env(name: str, default: int) -> int:
+    try:
+        return int(os.getenv(name, default))
+    except Exception:
+        return default
+
+def _get_bool_env(name: str, default: bool) -> bool:
+    v = os.getenv(name)
+    if v is None:
+        return default
+    return v.lower() in ("1", "true", "yes", "y")
+
+
 class ModalityAwareAggregation(fl.server.strategy.Strategy):
@@ -162,2 +176,8 @@ """
 
+        # --- student gating knobs (can be overridden by env) ---
+        self.use_student = _get_bool_env("FEDMAP_USE_STUDENT", True)
+        self.student_warmup = _get_int_env("FEDMAP_STUDENT_WARMUP", 5)      # rounds before student can dominate
+        self.student_win_needed = _get_int_env("FEDMAP_STUDENT_WIN_NEEDED", 3)  # consecutive wins needed
+        self._student_streak = 0  # internal counter
+
     # ---------------- Flower Strategy API ----------------
@@ -345,6 +365,88 @@
 
-        # Convert best NDArrays -> Parameters
-        nds_final = best["nds"]
-        if nds_final is None:
-            # Fallback to uniform averaging (or student attention if valid)
+        # --- evaluate student attention directly (single eval) ---
+        def _metric_score_from_nds(nds):
+            loss, metrics = self.evaluate_fn(server_round, nds, {})
+            return float(metrics.get("test_f1_macro", metrics.get("accuracy", 0.0)) or 0.0)
+
+        score_T = float(best["score"])
+        nds_S = aggregate_with_attention(new_attn)
+        score_S = _metric_score_from_nds(nds_S) if nds_S is not None else -1.0
+        print(f"📈 Teacher score={score_T:.6f} | Student score={score_S:.6f}")
+
+        # --------- Decide student vs teacher (KL-bound + probe + blend + streak gate) ---------
+        # Target max drop on server metric (e.g., macro-F1) compared to teacher
+        delta = 0.002  # tighten/loosen as desired
+
+        # Compute KL(teacher || student)
+        def _kl_t_s(p, q):
+            p = _safe_nan_to_num(p.to(_DEVICE))
+            q = _safe_nan_to_num(q.to(_DEVICE))
+            p = torch.clamp(p, 1e-12, 1.0)
+            q = torch.clamp(q, 1e-12, 1.0)
+            return (p * (p.log() - q.log())).sum()
+
+        kl_ts = _kl_t_s(_safe_nan_to_num(attn_teacher), _safe_nan_to_num(new_attn)).item()
+
+        # Small Lipschitz probe toward student to estimate local L
+        alpha = 0.1
+        with torch.no_grad():
+            w_alpha = _safe_nan_to_num((1 - alpha) * attn_teacher + alpha * new_attn)
+            nds_alpha = aggregate_with_attention(w_alpha)
+        score_alpha = _metric_score_from_nds(nds_alpha) if nds_alpha is not None else score_T
+
+        l1_step = torch.sum(torch.abs(w_alpha - attn_teacher)).item()
+        Lhat = abs(score_alpha - score_T) / max(l1_step, 1e-8)
+
+        import math
+        tau = 0.5 * (delta / max(Lhat, 1e-8)) ** 2  # Pinsker + Lipschitz bound
+
+        denom = math.sqrt(max(2.0 * kl_ts, 1e-16))
+        eta = 0.2  # larger → switch faster to student
+        beta = min(1.0, eta / denom) if denom > 0 else 1.0
+
+        # --- streak gate: student must be consistently good ---
+        student_ok = (score_S >= score_T - delta) and (kl_ts <= tau)
+
+        if student_ok:
+            self._student_streak += 1
+        else:
+            self._student_streak = 0
+
+        print(f"🧪 student_ok={student_ok} | streak={self._student_streak}/{self.student_win_needed} "
+              f"| warmup={self.student_warmup}")
+
+        if not self.use_student:
+            # hard-disable student: stick to teacher
+            w_final = attn_teacher
+            decision = "teacher-only (student disabled)"
+        elif server_round < self.student_warmup:
+            # warmup: allow only cautious blend toward student
+            beta_guarded = min(beta, 0.25)
+            w_final = _safe_nan_to_num((1 - beta_guarded) * attn_teacher + beta_guarded * new_attn)
+            decision = "teacher-dominant (warmup)"
+        elif self._student_streak >= self.student_win_needed:
+            # student has repeatedly matched/beaten teacher with small KL → let it dominate
+            beta_student = max(0.6, min(1.0, beta))  # strong lean to student
+            w_final = _safe_nan_to_num((1 - (1 - beta_student)) * new_attn + (1 - beta_student) * attn_teacher)
+            decision = "student-dominant (passed gate)"
+        else:
+            # default: trust-region blend using KL/Lipschitz logic
+            if kl_ts <= tau:
+                w_final = _safe_nan_to_num((1 - beta) * attn_teacher + beta * new_attn)
+                decision = "student/blend (KL small)"
+            else:
+                beta_guarded = min(beta, 0.25)
+                w_final = _safe_nan_to_num((1 - beta_guarded) * attn_teacher + beta_guarded * new_attn)
+                decision = "teacher-dominant (KL large)"
+
+        print(f"🔎 KL(T||S)={kl_ts:.6f}, Lhat={Lhat:.6f}, tau={tau:.6f}, beta={beta:.3f} → {decision}")
+
+        nds_blend = aggregate_with_attention(w_final)
+
+        # Prefer blend → then teacher → then student/uniform fallback
+        if nds_blend is not None:
+            nds_final = nds_blend
+        elif best["nds"] is not None:
+            nds_final = best["nds"]
+        else:
             n = len(weights_per_client)
@@ -384,7 +486,5 @@ fallback_attn = new_attn if new_attn.numel() == n else torch.ones(n, device=_DEVICE) / n
     def evaluate(self, server_round: int, parameters: fl.common.Parameters):
-         
-        
         if self.evaluate_fn is None:
             return None  # Flower will skip
-        
+
         nds = parameters_to_ndarrays(parameters)
@@ -391,0 +491,0 @@ loss, metrics = self.evaluate_fn(server_round, nds, {})

PKG-INFO

@@ -1,4 +0,4 @@
-Metadata-Version: 2.4
+Metadata-Version: 2.2
 Name: fedops
-Version: 1.1.30.4
+Version: 1.1.30.5
 Summary: FL Lifecycle Operations Management Platform
@@ -5,0 +5,0 @@ Home-page: https://github.com/gachon-CCLab/FedOps.git

setup.py

@@ -7,3 +7,3 @@ from setuptools import setup, find_packages
     name='fedops',
-    version='1.1.30.4',
+    version='1.1.30.5',
     author='Semo Yang',
@@ -10,0 +10,0 @@ author_email='[e-mail]',

Alert delta unavailable

Currently unable to show alert delta for PyPI packages.

Improved metrics

Total package byte prevSize

172322

2.88%

Lines of code

3400

2.66%