Salvaging Federated Learning by Local Adaptation

联合学习 计算机科学 差别隐私 适应(眼睛) 激励 任务(项目管理) 质量(理念) 方案(数学) 差速器(机械装置) 人工智能 机器学习 数据挖掘 心理学 数学分析 哲学 数学 管理 认识论 神经科学 工程类 经济 微观经济学 航空航天工程
作者
Changyuan Yu,Eugene Bagdasaryan,Vitaly Shmatikov
出处
期刊:Cornell University - arXiv 被引量:102
标识
DOI:10.48550/arxiv.2002.04758
摘要

Federated learning (FL) is a heavily promoted approach for training ML models on sensitive data, e.g., text typed by users on their smartphones. FL is expressly designed for training on data that are unbalanced and non-iid across the participants. To ensure privacy and integrity of the fedeated model, latest FL approaches use differential privacy or robust aggregation. We look at FL from the \emph{local} viewpoint of an individual participant and ask: (1) do participants have an incentive to participate in FL? (2) how can participants \emph{individually} improve the quality of their local models, without re-designing the FL framework and/or involving other participants? First, we show that on standard tasks such as next-word prediction, many participants gain no benefit from FL because the federated model is less accurate on their data than the models they can train locally on their own. Second, we show that differential privacy and robust aggregation make this problem worse by further destroying the accuracy of the federated model for many participants. Then, we evaluate three techniques for local adaptation of federated models: fine-tuning, multi-task learning, and knowledge distillation. We analyze where each is applicable and demonstrate that all participants benefit from local adaptation. Participants whose local models are poor obtain big accuracy improvements over conventional FL. Participants whose local models are better than the federated model\textemdash and who have no incentive to participate in FL today\textemdash improve less, but sufficiently to make the adapted federated model better than their local models.
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