Federated learning (FL) allows multiple clients to collaboratively train a shared machine learning model without sharing local data. Despite its advantages, FL faces serious security and privacy threats. Many existing solutions rely on cryptographic methods to protect data and ensure verifiability, but these approaches often enlarge the model or impose high communication costs. They also overlook FL's limited uplink and downlink bandwidth and rarely account for practical issues such as client dropouts. To address these gaps, we propose LC-VPFL, a federated learning framework that ensures data privacy, verifiability, low communication overhead, and dropout tolerance. Our approach leverages secret sharing and masking to protect data privacy, while homomorphic hashing detects malicious server behavior. To minimize communication costs, we apply quantization and CUR matrix decomposition, optimizing both uplink and downlink transmissions. We formally prove the security of LC-VPFL and provide a theoretical analysis demonstrating that, for a corruption threshold of$t$, the communication complexity of partial clients remains$O(t)$and tolerates arbitrary client dropouts. Experimental results show that LC-VPFL reduces uplink communication costs by over 50% in most scenarios and downlink communication costs to less than 12.5% of those in FedAvg, with an accuracy loss within 3%.

Federated learning (FL) when deployed in an Internet of Things (IoT) ecosystem can facilitate the collaborative training of a global model involving different IoT local systems. However, there are a number of challenges in such deployments, and examples include single point of failure / attack, lack of fault tolerance, vulnerability to collusion attacks and accuracy loss. Therefore, we propose a privacy-preserving serverless FL scheme for IoT based on secure multi-party computation. Specifically, in our scheme, no central sever is required to coordinate the generation of global models. In doing so, we avoid the single point of failure / attack limitation. We also mitigate the fault tolerance limitation by using secret sharing. Finally, we provide a formal security proof that demonstrates the resilience of our scheme against collusion attacks, thereby establishing its effectiveness in achieving robust data privacy. Simulations are also implemented to show that our scheme does not suffer from accuracy loss.

Federated learning (FL) allows multiple users to collaboratively train global machine learning models by keeping their data sets local. However, the existing privacy-preserving FL schemes suffer from several limitations, e.g., loss of accuracy, high communication/computation cost, failure to support dynamic users, and insecurity against collusion attacks. To solve these limitations, we propose a lightweight privacy-preserving FL scheme based on a dual-server architecture. Our scheme involves only lightweight cryptographic operations, i.e., hash and symmetric encryption operations, and it has low communication overhead. Thus, it is computationally lightweight and round-efficient. Further, it allows users to join/quit an FL task and it is accuracy-lossless. We formally prove that our scheme remains secure even in case of collusion attacks. In particular, if an attacker colludes with one of the servers and all the users who participate in an FL task except two, the privacy of user gradients stays unviolated. The reported experimental results demonstrate that our scheme incurs only a marginal increase in total communication overhead compared to the FL scheme without any privacy protection. In terms of computation overhead, the cost per user remains stable as the number of users grows, while the cost for the server is comparable to that of the FL scheme without any privacy protection.