Show HN: I solo-validated Fed learning at 10M nodes with 50% Byzantine tolerance
I just finished testing a federated learning system at 10 million nodes. It maintains 82% accuracy even when 5 million nodes are malicious. Here's what happened ↓ --- *The Test (Feb 24, 2026)* 10,000,000 nodes 4,000,000 - 5,000,000 malicious (Byzantine) nodes 59 minutes 41 seconds total runtime 100% success rate Results: • 40% Byzantine (4M bad): 83.3% accuracy • 50% Byzantine (5M bad): 82.2% accuracy --- *Why this matters* Google's federated learning papers max out at ~10K nodes in production.
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来源记录
指标快照
points · 2 votes
2026-11-09 12:15:39.760Z
cumulative since publication
研究分析
变化记录
来源描述：I just finished testing a federated learning system at 10 million nodes. It maintains 82% accuracy even when 5 million nodes are malicious. Here's what happened ↓ --- *The Test (Feb 24, 2026)* 10,000,000 nodes 4,000,000 - 5,000,000 malicious (Byzantine) nodes 59 minutes 41 seconds total runtime 100% success rate Results: • 40% Byzantine (4M bad): 83.3% accuracy • 50% Byzantine (5M bad): 82.2% accuracy --- *Why this matters* Google's federated learning papers max out at ~10K nodes in production.
版本与成本证据
来源票数: 2 votes (2026年11月9日 UTC; cumulative since publication)；前次同口径记录为 2（2026年10月19日 UTC），两个观测值不等于需求增长。
适用与受影响对象
先明确变化影响的现有工作流，再判断是否升级。发布热度不证明效果提升；接口、价格或可用性变化也可能增加迁移工作。
缺失的比较信息
待补齐：新旧行为、受影响套餐与地区、生效时间、兼容性、回退路径及同一工作量下的成本。
采用与迁移验证
保存发布或政策原文，对新旧方案运行同一个任务并比较结果与完整账单；若涉及停服，先核验截止时间再安排迁移。