Han Zhang

Han Zhang, Halvin Yang, Guopeng Zhang

In vanilla federated learning (FL) such as FedAvg, the parameter server (PS) and multiple distributed clients can form a typical buyer's market, where the number of PS/buyers of FL services is far less than the number of clients/sellers. In order to improve the performance of FL and reduce the cost of motivating clients to participate in FL, this paper proposes to differentiate the pricing for services provided by different clients rather than simply providing the same service pricing for different clients. The price is differentiated based on the performance improvements brought to FL and their heterogeneity in computing and communication capabilities. To this end, a price-discrimination game (PDG) is formulated to comprehensively address the distributed resource management problems in FL, including multi-objective trade-off, client selection, and incentive mechanism. As the PDG is a mixed-integer nonlinear programming (MINLP) problem, a distributed semi-heuristic algorithm with low computational complexity and low communication overhead is designed to solve it. The simulation result verifies the effectiveness of the proposed approach.

Han Zhang, Zifan Wang, Mihir Dhamankar et al.

Many Internet-of-Things (IoT) devices rely on cloud computation resources to perform machine learning inferences. This is expensive and may raise privacy concerns for users. Consumers of these devices often have hardware such as gaming consoles and PCs with graphics accelerators that are capable of performing these computations, which may be left idle for significant periods of time. While this presents a compelling potential alternative to cloud offloading, concerns about the integrity of inferences, the confidentiality of model parameters, and the privacy of users' data mean that device vendors may be hesitant to offload their inferences to a platform managed by another manufacturer. We propose VeriSplit, a framework for offloading machine learning inferences to locally-available devices that address these concerns. We introduce masking techniques to protect data privacy and model confidentiality, and a commitment-based verification protocol to address integrity. Unlike much prior work aimed at addressing these issues, our approach does not rely on computation over finite field elements, which may interfere with floating-point computation supports on hardware accelerators and require modification to existing models. We implemented a prototype of VeriSplit and our evaluation results show that, compared to performing computation locally, our secure and private offloading solution can reduce inference latency by 28%--83%.

Ruonan Dong, Hui Xu, Han Zhang et al.

Federated Learning (FL) is a distributed machine learning paradigm that addresses privacy concerns in machine learning and still guarantees high test accuracy. However, achieving the necessary accuracy by having all clients participate in FL is impractical, given the constraints of client local computing resource. In this paper, we introduce a multi-user collaborative computing framework, categorizing users into two roles: model owners (MOs) and data owner (DOs). Without resorting to monetary incentives, an MO can encourage more DOs to join in FL by allowing the DOs to offload extra local computing tasks to the MO for execution. This exchange of "data" for "computing resources" streamlines the incentives for clients to engage more effectively in FL. We formulate the interaction between MO and DOs as an optimization problem, and the objective is to effectively utilize the communication and computing resource of the MO and DOs to minimize the time to complete an FL task. The proposed problem is a mixed integer nonlinear programming (MINLP) with high computational complexity. We first decompose it into two distinct subproblems, namely the client selection problem and the resource allocation problem to segregate the integer variables from the continuous variables. Then, an effective iterative algorithm is proposed to solve problem. Simulation results demonstrate that the proposed collaborative computing framework can achieve an accuracy of more than 95\% while minimizing the overall time to complete an FL task.