Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints (1910.01991v1)

Abstract: Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit it's popularity, it has been observed that Federated Learning yields suboptimal results if the local clients' data distributions diverge. To address this issue, we present Clustered Federated Learning (CFL), a novel Federated Multi-Task Learning (FMTL) framework, which exploits geometric properties of the FL loss surface, to group the client population into clusters with jointly trainable data distributions. In contrast to existing FMTL approaches, CFL does not require any modifications to the FL communication protocol to be made, is applicable to general non-convex objectives (in particular deep neural networks) and comes with strong mathematical guarantees on the clustering quality. CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy preserving way. As clustering is only performed after Federated Learning has converged to a stationary point, CFL can be viewed as a post-processing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models. We verify our theoretical analysis in experiments with deep convolutional and recurrent neural networks on commonly used Federated Learning datasets.

An Expert Overview of Clustered Federated Learning

The paper "Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints" addresses pertinent limitations in Federated Learning (FL) when local clients' data distributions are not congruent. The authors present Clustered Federated Learning (CFL) as a novel Federated Multi-Task Learning (FMTL) framework designed to optimize the training in distributed systems with privacy constraints, resolving challenges inherent in conventional FL.

Federated Learning and Its Limitations

Federated Learning enables multiple clients to collaboratively train a machine learning model on their combined data without sharing the actual datasets, thus preserving privacy. FL operates by performing local optimizations on client devices followed by aggregating these updates at a central server. However, a key assumption of this setup is that a single model can fit the diverse data distributions of all clients. This assumption often fails in practice, as illustrated by cases such as clients with varying preferences or demographic-based text data, where a single model cannot generalize well across all clients' distributions.

The Proposal of Clustered Federated Learning

CFL extends the FL paradigm by clustering clients into groups with congruent data distributions. This process ensures that within each cluster, a specialized model is trained, thereby improving the overall performance. The CFL methodology capitalizes on the geometric properties of the FL loss surface to identify clusters post-FL convergence.

Theoretical Underpinnings

The paper's core contribution is the theoretical framework underpinning CFL. The authors derive conditions under which the cosine similarity between weight updates of client models can reveal natural clusters in the data distributions. They prove the following:

1. Cosine Similarity for Clustering: The similarity of gradient updates (measured via cosine similarity) among clients can reliably distinguish between congruent and incongruent data distributions.
2. Guaranteed Correct Clustering: Under the presented assumptions, there exists a provably correct bi-partitioning of clients that maximizes intra-cluster similarity and minimizes inter-cluster similarity.
3. Mathematical Validations: The Derived Separation Theorem provides bounds on the cosine similarity, ensuring that clustering performed using these metrics is mathematically sound.

Practical Implementation and Experimental Validation

The implementation of CFL considers practical aspects such as the communication constraints and the need for privacy-preserving mechanisms. The authors propose strategies for efficient computation of cosine similarity using weight updates, enhancing the method's applicability without altering the existing FL communication protocol.

Experimental results on datasets such as MNIST and CIFAR-10 robustly validate CFL's efficacy. The experiments demonstrate that CFL achieves superior performance compared to standard FL by accurately dividing clients into clusters based on their data distribution. Notably, CFL significantly enhances model accuracy, thereby underscoring its practical benefits.

Implications and Future Directions

The implications of CFL are noteworthy both theoretically and practically. Theoretically, CFL provides a framework for distributed learning on heterogeneous data with guarantees on clustering quality. Practically, it ensures model performance gains by allowing data distributions' natural clustering to drive the learning process.

Future research could explore extending the principles of CFL to other optimization algorithms and further refining clustering mechanisms to accommodate dynamic client populations and varying data characteristics. Another promising direction includes investigating how CFL can be integrated with techniques for compressed weight updates or compact parameter representations to further optimize communication efficiency.

Overall, CFL represents a significant advancement in federated learning, particularly in scenarios involving non-iid data distributions. Its practical utility is evident in its ability to maintain privacy without sacrificing model accuracy, making it an essential development in the field of privacy-preserving machine learning.