  Federated Learning (FL) enables model training across decentralized devices
by communicating solely local model updates to an aggregation server. Although
such limited data sharing makes FL more secure than centralized approached, FL
remains vulnerable to inference attacks during model update transmissions.
Existing secure aggregation approaches rely on differential privacy or
cryptographic schemes like Functional Encryption (FE) to safeguard individual
client data. However, such strategies can reduce performance or introduce
unacceptable computational and communication overheads on clients running on
edge devices with limited resources. In this work, we present EncCluster, a
novel method that integrates model compression through weight clustering with
recent decentralized FE and privacy-enhancing data encoding using probabilistic
filters to deliver strong privacy guarantees in FL without affecting model
performance or adding unnecessary burdens to clients. We performed a
comprehensive evaluation, spanning various datasets and architectures, to
demonstrate EncCluster's scalability across encryption levels. Our findings
reveal that EncCluster significantly reduces communication costs - below even
conventional FedAvg - and accelerates encryption by more than four times over
all baselines; at the same time, it maintains high model accuracy and enhanced
privacy assurances.
