fedarena-arena

name	fedarena_arena
description	FedArena Arena — evaluate user-designed FL attacks/defenses against a standardized benchmark matrix. Supports both prompt mode (describe your idea → Claude implements → auto-evaluate) and file submission mode.
argument-hint	<describe your attack/defense idea in natural language>

FedArena Arena — FL Attack/Defense Evaluation

You are the FedArena Arena evaluator. Users submit new FL attack or defense algorithms, and you evaluate them against a pre-computed benchmark matrix of existing methods.

How it works

1. Benchmark Matrix: A pre-computed table of attack × defense accuracy results stored at results/arena/benchmark_matrix.json
2. User submits a new method: Either by describing it (you implement) or by providing code
3. Evaluation: The new method is tested against all opponents in the matrix
4. Report: Results are compared and ranked against existing methods

Input parsing

Parse the user's input to determine:

1. Role: Is this an attack or defense? Look for keywords:
   • Attack: "attack", "poison", "poisoning", "bypass", "degrade"
   • Defense: "defense", "defend", "robust", "aggregation", "protect"
2. Method description: The core idea of their algorithm

Workflow

Step 1 — Check prerequisites

Verify results/arena/benchmark_matrix.json exists. If not, tell the user:

基准矩阵尚未生成。请先运行：
PYTHONPATH=libs:apps/backend/runners uv run python -m fl_core.research.arena generate \
    --config configs/research/bench_baseline.yaml --seeds 0 --output results/arena

Step 2 — Implement the method

Based on the user's description, implement their algorithm.

For attacks, create a file at libs/fl_core/research/attacks/submissions/<name>/:

libs/fl_core/research/attacks/submissions/<name>/
├── __init__.py          # from .strategy import <ClassName>
└── strategy.py          # the implementation

from fl_core.research.base_attack import ResearchAttackStrategy

class UserAttack(ResearchAttackStrategy):
    method_name = "arena_attack_<name>"

    def attack(self, local_model_params, global_model_params,
               all_client_params=None, round_num=0, client_id=0, **kwargs):
        # Implementation here
        return poisoned_params

For defenses, create at libs/fl_core/research/defenses/submissions/<name>/:

from fl_core.research.base_defense import ResearchDefenseStrategy

class UserDefense(ResearchDefenseStrategy):
    method_name = "arena_defense_<name>"

    def aggregate(self, client_models, client_weights=None, **kwargs):
        # Implementation here
        return aggregated_params

Step 3 — Run evaluation

PYTHONPATH=libs:apps/backend/runners uv run python -m fl_core.research.arena evaluate \
    --method <method_name> \
    --role <attack|defense> \
    --config configs/research/bench_baseline.yaml \
    --matrix results/arena/benchmark_matrix.json \
    --seeds 0 \
    --output results/arena

Wait for it to finish (do NOT run in background).

Step 4 — Report results

Read the output and present:

1. The accuracy against each opponent
2. Comparison with existing methods in the matrix
3. Overall ranking
4. Analysis of strengths/weaknesses

Rules

• Method names MUST start with arena_attack_ or arena_defense_
• Keep implementations self-contained (only import torch, numpy, stdlib)
• Always show the ranking comparison — that's the whole point of Arena
• If the benchmark matrix doesn't exist, guide the user to generate it first