Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
This addresses utility degradation in federated learning for scenarios with varying client privacy needs and data sizes, representing an incremental improvement over existing methods.
The paper tackles the problem of heterogeneous privacy requirements and data sizes in differentially private federated learning, which degrade utility, by proposing Robust-HDP, an algorithm that estimates and reduces noise in aggregated model updates, improving utility and convergence speed as confirmed by experiments and theory.
High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has been tried to achieve by using differential privacy in FL (DPFL). There is often heterogeneity in clients privacy requirements, and existing DPFL works either assume uniform privacy requirements for clients or are not applicable when server is not fully trusted (our setting). Furthermore, there is often heterogeneity in batch and/or dataset size of clients, which as shown, results in extra variation in the DP noise level across clients model updates. With these sources of heterogeneity, straightforward aggregation strategies, e.g., assigning clients aggregation weights proportional to their privacy parameters will lead to lower utility. We propose Robust-HDP, which efficiently estimates the true noise level in clients model updates and reduces the noise-level in the aggregated model updates considerably. Robust-HDP improves utility and convergence speed, while being safe to the clients that may maliciously send falsified privacy parameter to server. Extensive experimental results on multiple datasets and our theoretical analysis confirm the effectiveness of Robust-HDP. Our code can be found here.