Privacy-Preserving Active Learning for circular manufacturing supply chains for extreme data sparsity scenarios

说明与用途

一种结合差分隐私、主动学习和联邦聚合的原型系统，从循环制造供应链中极度稀疏、私有的数据中提取有用信号。

问题

Sparse, highly sensitive labeled data across a distributed network of recyclers and remanufacturers makes federated learning ineffective, as local models overfit and aggregated updates degrade performance.

结果

The integrated system successfully extracted signal from extremely sparse, private data, demonstrating that DP‑protected active learning can operate in federated settings where naive averaging fails.

工作流

• Central orchestrator maintains global model and aggregates DP‑noised updates; broadcasts acquisition scores to clients; each client runs local DP‑SGD on labeled data, computes uncertainty scores on unlabeled pool; DP‑protects acquisition scores with a smaller privacy budget; clients send DP‑noised updates and scores back; orchestrator aggregates updates and updates global model; repeat until convergence or budget exhausted.

证据

While exploring the intersection of federated learning and circular economy systems, I stumbled upon a problem that genuinely kept me up at night.

来源

The fix isn't more data—it's smarter querying of the data you already have.

来源

acquisition scores themselves leak information. If a client reports "sample #47 is highly uncertain," that reveals something about the local data distribution.

来源