Unverified50% confidenceFactExact time
Data heterogeneity in federated settings is often called the Non-IID problem, i.e., the data distributions of different clients are inconsistent.
1
Sources
50%
Confidence
Long-term
Relevance
10/20/2026
First Seen
Sources
Related Entities
Related Claims
Unverified错误的归纳偏置会让模型花费容量拟合数据中不存在的虚假模式,导致分布外数据上严重失效66% similarUnverifiedWhen statistical heterogeneity exists in the data distributions of clients, the global model tends to cater to clients with larger data volumes and mainstream distributions, causing clients that deviate from the mainstream to exhibit significantly worse local accuracy.65% similarUnverified向量数据库通常缺乏原生的行级权限支持,需要在相似度计算时附加元数据过滤条件64% similarUnverifiedReliable abstention capability across datasets remains an unsolved problem, as methods that work on one dataset may fail on another.61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1074610API
curl https://kongchang.com/api/v1/knowledge/claims/1074610MCP
get_claim(id=1074610)