Federated learning can indeed work with unsupervised learning tasks. Federated learning is a decentralized approach where multiple devices or servers train a model collaboratively without sharing raw data. In unsupervised learning, the goal is to find patterns or structure in unlabeled data, such as clustering or dimensionality reduction. The core idea of federated learning—keeping data local and sharing only model updates—applies equally here. For example, a group of hospitals could collaboratively train a clustering model to identify patient subgroups using their local datasets without exposing sensitive health records. The key is designing algorithms that aggregate local model updates effectively while preserving the privacy and efficiency benefits of federated learning.
A practical example is federated clustering. Suppose multiple devices collect sensor data, and the task is to group similar data points without labels. Each device could run a clustering algorithm (like K-means) locally, compute cluster centroids, and send these centroids to a central server. The server then aggregates the centroids (e.g., by averaging or merging overlapping clusters) and sends the updated centroids back to the devices for the next iteration. Another example is federated autoencoders for anomaly detection. Devices train autoencoders locally to reconstruct their data, and the server aggregates the model weights. The global model learns a shared representation of “normal” data, enabling anomaly detection across all devices. These approaches require careful handling of non-IID data (where local data distributions differ) and alignment of local model outputs during aggregation.
Challenges arise in ensuring consistency and avoiding divergence. For instance, if devices have vastly different data distributions, local clusters or feature representations might not align, leading to a fragmented global model. Techniques like regularization (to penalize deviations from the global model) or dynamic weighting (prioritizing devices with higher-quality updates) can help. Communication efficiency is also critical—unsupervised tasks often involve larger models (e.g., deep autoencoders), so compressing updates or using sparse aggregation methods may be necessary. Privacy remains a concern; even without sharing raw data, aggregated model updates could leak information. Differential privacy or secure multi-party computation can mitigate this. While federated unsupervised learning is feasible, its success depends on tailoring algorithms to address these challenges while maintaining the core benefits of federated learning.