The edges of a network can be divided into positive and negative relationships according to their potential meanings. When the edges of a network are signed with plus or minus signs respectively, a signed network can be formed. Signed networks are widely used in many fields such as sociology, informatics and biology. Hence, the sign prediction problem in signed networks has become one of research hot spots. In large dataset, the scalability of sign prediction algorithm is still a great challenge. There are many related works in the distributed design of signed network prediction methods, however, the computation efficiency is still limited by the fundamental server/client framework. This paper proposes client to client distributed framework (C2CDF). Compared with traditional server/client framework, C2CDF is a completely new client-to-client framework which can release the bandwidth pressure by abandoning the server node and allowing the communications between the client nodes. The Experiments on sign prediction in signed social networks, prediction in click-through rate and prediction in forest type show that C2CDF is a general approach which can not only be applied in sign prediction in signed network but also be used in the other prediction areas. In these three datasets, C2CDF can achieve better performance than FM inferred by the traditional SGD algorithm. C2CDF also achieves a 2.3-3.3x speed-up over the method implemented under the server/client framework while obtains a better accuracy performance than the method compared against.