SURGE: Sparse Updates With Randomized Guarding and Selective Encryption for Secure Federated Learning

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

This work proposes PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation, and improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.

NETFL: Network Efficient Fully Secure Federated Learning at Scale

SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring

Federated Learning (FL) enables privacy-aware distributed training, yet gradient updates remain exploitable: Man-in-the-Middle (MitM) interception exposes updates in transit, while model poisoning corrupts global convergence. We first introduce GASHE (Gradient-Aware Selective Homomorphic Encryption), a novel selective encryption strategy that dynamically identifies and encrypts only the gradient components exceeding a DP-calibrated sensitivity threshold, rather than encrypting all parameters uniformly as in static layer-based or full-parameter CKKS schemes. Building on GASHE, we introduce SecureDrive-FL, a federated driver monitoring framework that couples DP-SGD with GASHE to create the first closed-loop DP+HE privacy pipeline: DP-SGD calibration parameters directly derive the GASHE encryption mask, unifying training-time privacy and communication-time confidentiality. Evaluated on a ten-class distracted driver classification task under non-IID federated splits, SecureDrive-FL matches DP-SGD alone's poisoning resistance (73.6% vs. 74.0% accuracy, 3.9% Attack Success Rate for both) while additionally withstanding MitM interception, where DP-SGD alone collapses to near-random accuracy (78.2% vs. 10.4%), all under only approx. 8--10% additional runtime overhead relative to DP-SGD alone---under DP-SGD noise injection with per-round privacy parameter epsilon_0=4.

DMSA-FL: secure and robust federated learning via double-masked secure aggregation with multiparty homomorphic encryption

Privacy preserving federated learning (FL) faces a crucial trade-off between privacy and efficiency. Specifically, traditional multi-party homomorphic encryption incurs high computational and communication costs, while schemes relying on a single mask are susceptible to selective aggregation attacks. To address these limitations, this paper proposes DMSA-FL as a practical and robust FL framework leveraging double-masked secure aggregation integrated with multiparty homomorphic encryption. We mitigate efficiency bottlenecks by designing a strategy that encrypts only low-dimensional mask seeds rather than high-dimensional gradients. This method utilizes a homomorphic pseudorandom generator for mask expansion. Furthermore, a robustness protocol based on threshold secret sharing over sparse sketches is implemented to ensure aggregation continuity during user dropouts. Theoretical analysis demonstrates that DMSA-FL is secure against collusion attacks and selective aggregation attacks. Extensive experiments on multiple datasets indicate that DMSA-FL maintains model accuracy comparable to plaintext baselines. Compared with representative federated learning schemes based on CKKS multiparty homomorphic encryption, our approach achieves improved computational efficiency and significantly reduces communication overhead. Moreover, the computational advantage becomes increasingly evident as the number of model parameters grows.

A Secure Aggregation Scheme Based on Additive Secret Sharing in Federated Learning

Federated learning alleviates data silos through a “data-local, model-global” paradigm, but transmitting plaintext gradients exposes clients to reconstruction attacks from malicious servers. Existing secure aggregation methods face trade-offs among privacy, accuracy, and efficiency: homomorphic encryption incurs high overhead, differential privacy sacrifices accuracy, and lightweight secret-sharing schemes often lack weighted aggregation support and suffer accuracy degradation as client numbers grow. To address these limitations, we propose SecAGG, a lossless secure weighted aggregation scheme based on additive secret sharing. SecAGG adopts a three-tier architecture consisting of client clusters, cooperative servers, and a super server. Clients split weighted model parameters into random shares and distribute them to cooperative servers, which perform encrypted partial aggregation before the super server securely reconstructs the global model. Experimental results demonstrate that SecAGG achieves strict security against up to M-1 colluding servers under the semi-honest model while preserving FedAvg-equivalent accuracy with minimal computation and communication overhead, effectively balancing privacy, accuracy, and efficiency.

Adversarial Latency Watermarking: Covertly Encoding Zero Bit Keys in Federated Learning Model Updates

These findings expose time as a first class security primitive in FL systems and recommend integrating timing randomization, lightweight monitoring, and scheduler level defenses into frameworks such as Tensor Flow Federated.