Pid tuning experiments#12
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- Keep Multi-Krum k parameter sensitivity: k=7,8,9 - Keep PID threshold sensitivity: 1.0σ, 1.5σ, 2.5σ - Keep PID optimized combo (early removal + tighter threshold) - Remove Trust, Trimmed Mean, and other PID variations - Change preserve_dataset to false for proper cleanup - Limit to 7 strategies for clear graph visualization
- Create femnist_pid_tuning_niid.json with same 7 strategies as IID - Change dataset_keyword from femnist_iid to femnist_niid - Update simulation_runner.py to use NIID config
- Increase thresholds: 1.5σ, 2.0σ, 3.0σ (was 1.0σ, 1.5σ, 2.5σ) - NIID data has higher variance, needs more conservative thresholds - Fixes ZeroDivisionError from removing all clients in round 5
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This branch contains all completed work for the federated learning defense analysis:
Experiments Completed:
Key Findings:
Deliverables Ready: